DOI: 10.5061/dryad.83bk3jb62
title: Collective self-assessment in banded mongoose intergroup contests

description: Contests over resources are widespread in nature. To optimize outcomes,
 animals assess fighting abilities, deciding to escalate conflicts based on
 their own strength (self-assessment) or comparing their own strength with
 that of their rival (mutual assessment). While most research focuses on
 one-on-one (dyadic) contests, the assessment strategies employed by groups
 remain poorly understood, even though animal groups from ants to humans
 engage in intergroup conflict. Mutual assessment is frequently assumed, as
 more information is thought to improve decision-making; however, this
 assumption has rarely been tested. Here, we used a dataset spanning 21
 years and 633 intergroup contests in a banded mongoose (Mungos mungo)
 population in Queen Elizabeth National Park, Uganda. Our results support a
 model of self-assessment: groups with many males tend to escalate
 conflicts regardless of the rival group's strength, thus contrasting
 the commonly held assumption that decisions during intergroup contests are
 made by mutual assessment. We suggest that assessing rival group strength
 during conflict could be disproportionately costly, compared with
 assessing own group strength, which can be done over longer time periods
 and is easier to obtain. Greater understanding of these dynamics can shed
 light on the drivers and escalation patterns of intergroup conflict across
 social species, including humans.
description: Study Population and Data Collection All data
 were collected as part of the Banded Mongoose Research Project, a
 long-term study on a population of banded mongooses in and around Mweya
 Peninsula, Queen Elizabeth National Park, Uganda (0°12′S, 29°54′E). This
 study includes data collected from 19<sup>th</sup> February
 2000 to 1<sup>st</sup> April 2021, encompassing 633 intergroup
 contests from 39 different groups and 76 unique pairings of groups. In
 general, at any given time, there are approximately 250 individuals
 present within the population, making up 10-12 groups consisting of around
 10-30 adults each (Cant, 2000; Cant et al., 2013). Every 1-3 days,
 researchers visited groups to record data on life-history (e.g., births,
 deaths), composition of each group, and details about their intergroup
 interactions (below). Scoring Intergroup Contest
 Intensity and Escalation Intergroup contests were
 recorded opportunistically as they occurred. Intergroup contests were
 defined as any time when at least two groups directed agonistic behavior
 towards each other (Fig. 2). Data on contest duration were not available.
 Intensity and escalation of intergroup contests were used instead as a
 proxy for cost and scored using comments recorded by the Banded Mongoose
 Research Project team. As an intergroup contest often involves a series of
 behaviors in which intensity escalates until one or both groups retreat
 (at which point the intergroup contest ends), the highest point of
 escalation was used as the level of intensity for each contest. The group
 that retreated was defined as the loser group. For ease of analysis, the
 range of intensities possible during an intergroup contest was divided
 into two categories (Fig. 2). The lowest level of intensity was
 “non-physical,” which was defined as an instance where two groups directed
 non-physical agonistic behavior (e.g., vocal and visual displays; Cant et
 al., 2016) towards each other and/or fled upon sighting. If this then
 escalated to fighting between the two groups in which there was aggressive
 physical contact (e.g., biting, scratching, wrestling) (Cant et al.,
 2016), this was defined as “physical”. Intergroup contests that escalate
 to physical combat are expected to have higher contest costs (such as
 energy used and injury risk) compared to non-physical contests (Green
 & Patek, 2018; Lane & Briffa, 2017; McGinley et al.,
 2015). Because the original dataset did not include enough information on
 whether only one group attempted to escalate, escalation was assumed to
 reflect a decision (or at least participation) by both groups, regardless
 of which group initiated the interaction. This approach follows the logic
 that even losing groups must have engaged physically to some degree and
 therefore incurred the associated energetic and injury risks. A more
 detailed behavioral analysis (e.g., whether only a subset of individuals
 from each group participated at any stage) was unavailable, but notably,
 some dyadic contest studies have used overall metrics of escalation to
 test assessment in a similar fashion (e.g., Green & Patek, 2018).
 Our scoring approach was then translated into a binomial escalation metric
 with 0 representing non-physical and 1 representing physical. Injurious
 and lethal violence in which aggressive physical contact results in severe
 injury or death of one or more individuals was included in the “physical”
 category, rather than a category of its own. This is because, firstly,
 injury and mortality were data-poor (9.4% of all intergroup interactions;
 N = 60), but more importantly, whether individuals suffer an injury is not
 so much a decision as it is a (potentially random) outcome of such
 physical combat. By contrast, whether violence escalates from non-physical
 to physical combat is a decision the group may make. Therefore, our use of
 the escalation metric likely reflects the cost each group was willing to
 pay. For each intergroup contest, a qualitative comment
 (a description of the events of the contest) was recorded by observers in
 the field. These comments were then assessed by three researchers (CR, FM,
 DS) to evaluate whether the contest escalated into physical violence (with
 0 representing non-physical and 1 representing physical), or whether the
 comments did not allow us to determine whether or not the contest
 escalated (termed: “undeterminable”). Second, all researchers assigned a
 confidence score (1–3) to each of their categorizations of intensity
 score, reflecting their certainty in the intensity score. The confidence
 score was based on criteria such as the clarity and detail of the recorded
 comment, as well as contextual factors that might influence interpretation
 (e.g., visibility conditions or proximity to the event). A score of 1
 indicated high confidence, 2 indicated moderate confidence, and 3
 indicated low confidence. We ended up removing contests that scored with
 low confidence (3) from the dataset because they were missing data on
 other variables included as fixed effects in our analysis. The three
 researchers discussed any ambiguous comments in detail to assign an
 escalation and confidence score if possible. The discussion was guided by
 FM, who has over 27 years of experience observing and collecting field
 data on banded mongooses and managing the Banded Mongoose Research
 Project. We report models using both highly confident
 and moderately confident scores (1,2), and models including only highly
 confident scores (1).  Statistical Analysis
 All statistical analyses were carried out using
 <em>R</em> 4.01 (Team, 2021). Binomial escalation response
 data were analyzed using generalized linear mixed effect models (GLMMs)
 with a binomial error structure and a logit link function (Bolker et al.,
 2009) with the ‘lme4’ package (Bates et al., 2015). For both losing groups
 and winning groups, we tested the effect of two RHP predictor
 variables—number of adult males (&gt;6 months old) and age of the
 oldest male (days) (Green et al., 2022). Both variables were scaled with a
 mean of zero and unit variance using the scale function (Becker et al.,
 1988). In total, we ran two GLMM models, one model
 using data from high and moderate confidence scores, and another using
 data from only high confidence scores. The model for each confidence
 category is as follows (“~” represents “as predicted by”):
 escalation ~ loser number of adult males + loser age of oldest
 male + winner number of adult males + winner age of oldest male
 Year, winner group ID, loser group ID, and unique pairings of
 groups (winner group ID + loser group ID) were included as random
 intercepts in each model to account for repeated measures of intergroup
 contests between the same groups and group dyads. Test statistics were
 obtained using the ANOVA function and confidence intervals using
 bootstrapping. We present the p-values, chi-squared values, parameter
 estimates (β) on the logit-scale, standard errors, and confidence
 intervals of each GLMM model, and compare p-values and direction of
 parameter estimates to the assumptions of pure self-assessment, and mutual
 assessment (Fig. 1). The collective self-assessment hypothesis would be
 supported if there was a positive and statistically significant
 relationship between RHP predictor variables and escalation for losing
 groups (β&gt;0; p&lt;0.05) and a weaker relationship for winning
 groups (β&gt;0; no significance threshold) (Fig. 1A-B). The collective
 mutual assessment hypothesis would be supported if these same tests
 indicated a significant positive relationship for losing groups
 (β&gt;0; p&lt;0.05) and a significant negative relationship for
 winning groups (β&lt;0; p&lt;0.05) (Fig. 1C-D).
description: # Collective self-assessment in banded mongoose intergroup contests
 Dataset DOI:
 [10.5061/dryad.83bk3jb62](https://doi.org/10.5061/dryad.83bk3jb62)
 **Contact Information** Correspondence to
 [M.A.Cant@exeter.ac.uk](mailto:M.A.Cant@exeter.ac.uk) and [Dan.Sankey@newcastle.ac.uk](mailto:Dan.Sankey@newcastle.ac.uk) ## Description of the data and file structure All data were collected as part of the Banded Mongoose Research Project, a long-term study on a population of banded mongooses in and around Mweya Peninsula, Queen Elizabeth National Park, Uganda (0°12′S, 29°54′E). This study includes data collected from 19^th^ February 2000 to 1^st^ April 2021 encompassing 633 intergroup contests from 39 different groups and 76 unique pairings of groups. Every 1-3 days, researchers visited groups to record data on life-history (e.g., births, deaths), composition of each group, and details about their intergroup interactions. ## Code/software 2025_08_20_Final_Data.R R Version: R4.5.0 + RStudio-2025.05.1-513 with RTools45 **Required Packages:** *lme4* version 1.1-37 *ggplot2* version 3.5.2 *effects* version 4.2-4 *gridExtra* version 2.3 *readr* version 2.1.5 *car version 3.1-3* *lubridate* version 1.9.4 ### Files and variables #### File: 2025_08_20_Final_Data.R **Description:** Contains code used to carry out the analysis. #### File: data_final.csv **Description:** Contains data used in the final analysis. Cells in which data were not available are denoted by 'NA'. ##### Variables * n: entry number * date: date (dd/mm/yyyy) * focal: focal group ID * rival: rival group ID * winner: contest winner ID * foc.n_adults: focal group number of adults * foc.n_males: focal group number of males * foc.mean_age: focal group mean age (days) * foc.mean_male_age: focal group mean male age (days) * foc.max_age: focal group maximum (oldest) age (days) * foc.max_male_age: focal group maximum (oldest) male age (days) * riv.n_adults: rival group number of adults * riv.n_males: rival group number of males * riv.mean_age: rival group mean age (days) * riv.mean_male_age: rival group mean male age (days) * riv.max_age: rival group maximum (oldest) age (days) * riv.max_male_age: rival group maximum (oldest) male age (days) * comment: comments and field notes * focalWin: focal group win (true / false) * loser: loser group ID * intensity: Maximum level of intensity or escalation (visual, physical, lethal) * intensityConfidence: Confidence in the score of intensity (1=high, 2=moderate, 3=low) * datechar: date (dd/mm/yyyy) * dategroupfoc: date (yyyy/mm/dd) + focal group ID * dategroupriv: date (yyyy/mm/dd) + rival group ID * intensitybin: binary intensity score (1=physical / 0=non-physical) * foc.mean_weight: focal group mean weight (g) * foc.max_weight: focal group maximum weight (g) * riv.mean_weight: rival group mean weight (g) * riv.max_weight: rival group maximum weight (g) * foc.mean_male_weight: focal group mean male weight (g) * foc.max_male_weight: focal group maximum male weight (g) * riv.mean_male_weight: rival group mean male weight (g) * riv.max_male_weight: rival group maximum male weight (g) * unqpair: winner group ID + loser group ID * win.n_males: winner group number of males * win.max_male_age: winner group max male age (days) * lose.n_males: loser group number of males * lose.max_male_age: loser group max male age (days) * winID: winner group ID * loseID: loser group ID #### File: 2021_05_25_life_history.csv **Description:** life history data from 25/11/1991 to 03/05/2021 Cells in which data were not available are denoted by 'NA'. ##### Variables * DATE: dd/mm/yyyy * DATEN: Date number * PACK: Pack ID * INDIV: Individual ID * SEX: M = Male, F = Female, P = Pup * AGE CAT: Age category (AD = adult, AS = adult senior, INF = infant, NEO = neonate, OES = oestrus, PUP = pup, SUB = sub-adult) * STATUS: status of the individual * START/END: start or end of record * CODE: codes denoting life history events/status * EXACT: exact category certainty * LSEEN: last seen  * CAUSE: cause of life history event/status * LITTER: ID of pups born to an individual * PREV NAME: previous name * COMMENT: comments * EDITED: edited record comments * ID: ID * date_entered: date a record was entered * Latitude: latitude of life history event (decimal degrees) * Longitude: longitude of life history event (decimal degrees) #### File: focal_group_comp.csv **Description:** Focal group composition data Cells in which data were not available are denoted by 'NA'. ##### Variables * igi.event: igi event ID * date: date of the event (dd/mm/yyyy) * daten: date number * assign.focal: assigned as focal * focal: focal group ID * rival: rival group ID * winner: winner group ID * winner_determination: how the winner was determined * winner_certainty: certainty in the determination of winner * indiv: individual ID * dob: date of birth * sex: sex (M = male, F = female, P = pup) * age: age (days) * adult: adult (Yes or No) * pregnant: pregnant or not pregnant * weigh_daten: date, number of weight record * weight: weight (g) * weigh_daten_diff: weight difference between dates #### File: focal_igi_winloss.csv **Description:** Focal group IGI (intergroup interaction) composition and outcome data Cells in which data were not available are denoted by 'NA'. ##### Variables * igi.event: igi event ID * date: date of the event (dd/mm/yyyy) * daten: date number * assign.focal: assigned as focal * focal: focal group ID * rival: rival group ID * winner: winner group ID * winner_determination: how the winner was determined * winner_certainty: certainty in the determination of the winner * n_adults: number of adults * n_males: number of males * mean_age: mean age (days) * mean_male_age: mean male age (days) * max_age: max age (days) * max_male_age: max male age (days) * mean_weight: mean weight (g) * mean_male_weight: mean male weight (g) * max_weight: max weight (g) * max_male_weight: max male weight (g) #### File: rival_group_comp.csv **Description:** Rival group composition data Cells in which data were not available are denoted by 'NA'. ##### Variables * igi.event: igi event ID * date: date of the event (dd/mm/yyyy) * daten: date number * assign.focal: assigned as focal * focal: focal group ID * rival: rival group ID * winner: winner group ID * winner_determination: how the winner was determined * winner_certainty: certainty in the determination of the winner * indiv: individual ID * dob: date of birth * sex: sex (M = male, F = female, P = pup) * age: age (days) * adult: adult (Yes or No) * pregnant: pregnant or not pregnant * weigh_daten: date, number of weight record * weight: weight (g) * weigh_daten_diff: weight difference between dates #### File: rival_igi_winloss.csv **Description:** Rival group IGI (intergroup interaction) composition and outcome data Cells in which data were not available are denoted by 'NA'. ##### Variables * igi.event: igi event ID * date: date of the event (dd/mm/yyyy) * daten: date number * assign.focal: assigned as focal * focal: focal group ID * rival: rival group ID * winner: winner group ID * winner_determination: how the winner was determined * winner_certainty: certainty in the determination of the winner * n_rival_adults: number of rival adults * n_rival_males: number of rival males * n_rival_females: number of rival females * n_rival_subs: number of rival subordinates * mean_rival_age: mean rival age (days) * mean_rival_male_age: mean rival male age (days) * max_rival_age: max rival age (days) * max_rival_male_age: max rival male age (days) * mean_rival_weight: mean rival weight (g) * mean_rival_male_weight: mean rival male weight (g) * max_rival_weight: max rival weight (g) * max_rival_male_weight: max rival male weight (g) #### File: combined_field_capture_weight_data.csv **Description:** Individual weight and pregnancy data Cells in which data were not available are denoted by 'NA'. ##### Variables * X: entry number * daten: date number * indiv: individual ID * group: group ID * sex: sex (M = male, F = female) * weight: weight (g) * pregnant: pregnant or not pregnant * type: type of measurement (field or capture) * dob: date of birth of the individual * age: age of the individual (days)