DOI: 10.5061/dryad.d0g0d
title: Data from: Self-organizing dominance hierarchies in a wild primate population

description: Linear dominance hierarchies, which are common in social animals, can
 profoundly influence access to limited resources, reproductive
 opportunities and health. In spite of their importance, the mechanisms
 that govern the dynamics of such hierarchies remain unclear. Two
 hypotheses explain how linear hierarchies might emerge and change over
 time. The ‘prior attributes hypothesis’ posits that individual differences
 in fighting ability directly determine dominance ranks. By contrast, the
 ‘social dynamics hypothesis’ posits that dominance ranks emerge from
 social self-organization dynamics such as winner and loser effects. While
 the prior attributes hypothesis is well supported in the literature,
 current support for the social dynamics hypothesis is limited to
 experimental studies that artificially eliminate or minimize individual
 differences in fighting abilities. Here, we present the first evidence
 supporting the social dynamics hypothesis in a wild population.
 Specifically, we test for winner and loser effects on male hierarchy
 dynamics in wild baboons, using a novel statistical approach based on the
 Elo rating method for cardinal rank assignment, which enables the
 detection of winner and loser effects in uncontrolled group settings. Our
 results demonstrate (i) the presence of winner and loser effects, and (ii)
 that individual susceptibility to such effects may have a genetic basis.
 Taken together, our results show that both social self-organization
 dynamics and prior attributes can combine to influence hierarchy dynamics
 even when agonistic interactions are strongly influenced by differences in
 individual attributes. We hypothesize that, despite variation in
 individual attributes, winner and loser effects exist (i) because these
 effects could be particularly beneficial when fighting abilities in other
 group members change over time, and (ii) because the coevolution of prior
 attributes and winner and loser effects maintains a balance of both
 effects.
description: Data for estimating the
 aggression intensity indexThis file contains a
 table with 4 columns: (1) wounds (number of observed wounds), (2) n_inter
 (number of observed interactions), (3) n_oestrus (number of estrus
 females) and (4) N_males (number of males). Each row represents an
 observation day.aggr_est_data.csvData for model fittingThis file contains a table with 11 columns: (1) Date (coded as an integer numbers, for each group the date of the first interaction is set to 0), (2) Group (a unique integer for each group), (3) Winner (individual that won the interaction; note that individuals are coded as an integer numbers that are group-specific), (4) Loser (individual that lost the interaction), (5) hybrid_w (a floating point number that quantifies the estimated degree of anubis ancestry of the winner) , (6) hybrid_l (the estimated degree of anubis ancestry of the loser), (7) Age_w (the winner’s age in years), (8) Age_l (the loser’s age in years), (9) Lag_day_w (the number of days since the last observed agonistic encounter of the winner), (10) Lag_day_l (the number of days since the last observed agonistic encounter of the loser), (11) aggr_index (the estimated aggression intensity index). Each row represents an observated interaction.ago_data.csvR script for running the extended Elo rating modelThis file contains the R code to fit the extended Elo model and plot k values. The code requires as input the file ‘ago_data.csv’ and the provided code needs to be modified to insert the correct path where this file was saved. The implemented plotting requires the R library ggplot2.extended elo rating model.r