Sequential phenotypic constraints on social information use in wild baboons

  1. Alecia J Carter  Is a corresponding author
  2. Miquel Torrents Ticó
  3. Guy Cowlishaw
  1. University of Cambridge, United Kingdom
  2. Institute of Zoology, United Kingdom
3 figures, 4 videos, 6 tables and 1 additional file

Figures

A visual representation of proximity methods used to define a connection.

The black arrow represents a connection via the 5 m nearest neighbour rule; the white lines, connections via the 5 m chain rule; and the white circle represents the 10 m threshold distance for a …

https://doi.org/10.7554/eLife.13125.004
Figure 2 with 1 supplement
Networks diagrams created from the 5 association rules in two troops of baboons.

Nodes (J troop: purple nodes, panels a-e; L troop: green nodes, panels f-j) represent individual baboons and edges between them indicate the strength of the measured relationship (see key). …

https://doi.org/10.7554/eLife.13125.005
Figure 2—figure supplement 1
The relationships between social network metrics (strength and betweenness) within and between social networks created with five different rules for defining a connection between individuals.

The rules were the 5 m chain rule (5 m), 10 m proximity (10 m), directed nearest neighbour (NN), directed grooming interactions (groom) and directed dominance interactions (dom). Colouration is …

https://doi.org/10.7554/eLife.13125.006
The relationships between social network centrality and successive steps of the social information process.

The relationships between social information (c, d) acquired, (e) applied and (f) exploited by wild baboons and their degree strengths in the social networks. Presented are the proximity networks …

https://doi.org/10.7554/eLife.13125.016

Videos

Video 1
Information diffusion experiments.

The video shows rapid diffusion of information about the location of the food patch after its initial discovery. In the first experiment, several individual baboons successively enter the patch and …

https://doi.org/10.7554/eLife.13125.007
Video 2
Information acquisition, application and exploitation.

The video shows an adult male monopolising the food patch, surrounded by juveniles who are obviously aware of the location of the patch, but cannot enter because of their lower rank. After the patch …

https://doi.org/10.7554/eLife.13125.008
Video 3
Tolerated queuing, co-feeding and vocal protest.

The first video (tolerated queuing) shows an adult male monopolising the food patch, with adult females and juvenile males and females queuing to check the patch after the male leaves. They enter …

https://doi.org/10.7554/eLife.13125.009
Video 4
Information diffusion through a social network.

The animation shows the diffusion of information about the location of one of the food patches through the 5 m proximity social network of L troop. The nodes are scaled to the ranks of the …

https://doi.org/10.7554/eLife.13125.011

Tables

Table 1

The information use sequence: definitions and examples.

https://doi.org/10.7554/eLife.13125.003
StageDefinitionExample(s) of stage
AcquisitionAn individual gains knowledge1. Gaining knowledge of the location of a food patch.
2. Gaining knowledge of the location or form of a novel task.
ApplicationAn individual uses the information that it has acquired in a relevant (but not necessarily successful) way1. Entering a food patch. Because information can become outdated, ‘application’ can occur even after the patch has been fully depleted, leading to no reward.
2. Using stimulus or local enhancement to manipulate a novel task, but not necessarily successfully.
ExploitationAn individual successfully uses information that it has acquired and applied to gain a benefit1. Gaining food from a patch.
2. Solving a novel task.
Table 2

Results of Spearman rank correlations testing whether there is a correlation between strengths and betweennesses in social networks created with different proximity and interaction rules. Presented …

https://doi.org/10.7554/eLife.13125.010
RuleSρp
5 m chain217907.6-0.57<0.001
10 m220939.0-0.59<0.001
Nearest neighbour directed81586.80.41<0.001
Nearest neighbour9218.60.430.003
Groom directed98412.90.300.005
Groom15346.00.050.72
Dominance directed67342.10.51<0.001
Dominance7950.40.51<0.001
Table 3

Correlation matrix of the phenotypes used in the analyses. Presented are the Spearman’s rank correlation (S) estimates.

https://doi.org/10.7554/eLife.13125.012
PhenotypeSexaAgeRankBoldnessProximitybGroomc
Sex1
Age-0.491
Rank0.430.161
Boldness0.16-0.55-0.241
Proximity0.29-0.60-0.020.511
Groom-0.580.630.15-0.46-0.291
  1. acoded as an integer: females = 0, male = 1.

  2. b, cRefer to strength in the identified network.

Table 4

Comparisons of the additive and multiplicative OADA models with social transmission versus the asocial learning model.

https://doi.org/10.7554/eLife.13125.013
ModelAdd/MultiPredictor networkdfLogLikAICC
Social transmissionAdd10 m51984.73979.4
Social transmissionMulti51979.43968.9
Social transmissionAdd5 m51992.93995.9
Social transmissionMulti51991.73993.4
Social transmissionAddNN directed52037.44085.0
Social transmissionMulti52036.64083.3
Social transmissionAddNN52024.44059.0
Social transmissionMulti52020.54051.0
Social transmissionAddGroom directed52045.74101.4
Social transmissionMulti52043.04096.1
Social transmissionAddGroom52045.24100.6
Social transmissionMulti52044.84099.7
Social transmissionAddDom directed52076.84163.8
Social transmissionMulti52076.84163.8
Social transmissionAddDom52076.64163.3
Social transmissionMulti52076.74163.6
Asocial learning--42076.84161.8
  1. The predictor networks were the 10 m rule (10 m), 5 m chain rule (5 m), both of which were undirected, directed and undirected nearest neighbour rule (NN), directed and undirected grooming interactions (Groom) and directed and undirected dominance interactions (Dom). Presented are the models, degrees of freedom (df), -log-likelihoods (LogLik), corrected Akaike information criteria (AICC). Add/Multi refers to whether the model was additive (Add) or multiplicative (Multi).

Table 5

Parameter estimates of individual-level variables of the competing OADA models for asocial effects on social transmission in the 10 m networks.

https://doi.org/10.7554/eLife.13125.014
ModelCoefficientEstimateS.E.
1Social transmission0.999
Sex0.1320.107
Rank0.4980.174
Age-0.0240.012
2Social transmission0.999
Boldness0.0010.001
Age-0.0200.011
3Social transmission0.999
Sex0.3280.086
4Social transmission0.999
Sex0.2500.093
Rank0.3530.158
  1. Presented are the bounded social transmission estimates (for completeness), the fixed effects in the models and their standard errors (S.E.).

Table 6

Parameter estimates of the minimal models investigating the effect of proximity and grooming strength on social information (i) acquisition, (ii) application and (iii) exploitation.

https://doi.org/10.7554/eLife.13125.015
ResponsePredictorEffect sizeS.E.tP
Social information acquisitionIntercept0.230.211.110.27
Proximity strength0.660.078.87<0.001
Social information applicationIntercept-1.300.38-3.40<0.001
Proximity strength0.650.106.45<0.001
Grooming strength0.01<0.0014.74<0.001
Sexa0.840.155.48<0.001
Social information
exploitation
Intercept-2.430.39-6.17<0.001
Grooming strength0.02<0.0014.67<0.001
Sexa0.730.332.210.03
Boldness0.01<0.0013.74<0.001
Rank1.390.512.710.01
  1. Presented are the predictor variables, their effect sizes, standard errors (S.E.), t values and p-values.

  2. aReference category: female

Additional files

Supplementary file 1

Comparisons of the different models used to assess the effect of individual-level variables on the transmission of information among individuals, with ∆AICc <2 considered to have good support.

Shown are the combinations of individual-level variables, whether the model was multiplicative, additive or neither (when the individual-level variables were not included) (model type), whether individual-level variables were included (0 = no, 1 = yes); whether the process was modelled as a social or asocial diffusion (social/asocial); the AICc of the model; the difference from the AICc of the best model (∆AICc); the support for the model (model weight); and the relative weight of the model in comparison to the model set.

https://doi.org/10.7554/eLife.13125.017

Download links