Research ArticleEVOLUTIONARY BIOLOGY

Intra- versus intergroup variance in collective behavior

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Science Advances  02 Jan 2019:
Vol. 5, no. 1, eaav0695
DOI: 10.1126/sciadv.aav0695
  • Fig. 1 Experimental procedure.

    (A) A single locust or a group of 10 locusts was introduced into a ring-shaped arena, with a barcode tag attached to each animal’s pronotum. Video monitoring and offline analysis enabled following the position of all locusts accurately and consistently throughout the experiment. Photo credit: Daniel Knebel, Tel Aviv University. (B) Data comprised three types: single animals in the arena (singles), groups of 10 animals in the arena (real groups), and fictive groups constructed by shuffling the data of the real groups (shuffled groups).

  • Fig. 2 Collective behavior in groups of 10 locusts.

    Comparison of collectivity measures between the real and the shuffled groups throughout the experiments. (A) Average order parameter. (B) Collectivity parameter. (C) Spread measure. Each point represents data from a single experiment (n = 20). ***P < 0.001.

  • Fig. 3 Kinematic parameters of single locusts versus locusts in real groups.

    (A) The average fraction of time spent walking, (B) the average walking speed, (C) the average duration of a walking bout, and (D) the average pause duration of locusts in real groups and of single locusts. Significant differences are noted between the averages, but not the width of the distributions. *P < 0.05.

  • Fig. 4 Kinematics of real versus shuffled groups of locusts.

    (Aa to Ad) Comparisons between the average fraction of walking, average walking speed, average walking bout, and average pause duration of the real and shuffled groups, respectively. (Ba to Bd) Comparisons between the within groups’ IQR of the fraction of walking, average walking speed, average walking bout, and average pause duration of the real and shuffled groups, respectively. *P < 0.05, **P < 0.01, ***P < 0.001.

  • Fig. 5 Kinematics of real versus shuffled groups of locusts.

    (A) Probability of walking as a function of the number of other walkers for a single experiment. Each colored line represents the probability of a specific animal to walk conditioned on the number of other walking animals (0 to 9). The thick gray line shows the experimental average. The shaded area marks the probability of the locusts to walk when five or more others walk (termed P2W5). (B) The correlation between the average value of P2W5 obtained using only the first and only the second halves of the experiments. The high correlation suggests that this observable is a consistent individual behavioral characteristic. (C) Correlation between the order and the collectivity parameters with P2W5.

  • Fig. 6 Simulation kinematic and collectivity measures.

    The output of computational simulations was compared when the model was introduced with either the data of the locusts in the real experimental groups (real groups), the shuffled data (shuffled groups), and the homogeneous data (same for all group members) equal to the average value of each simulated group (homogenized within groups), or the average of all simulated groups (homogenized across groups). (A to D) Distribution of the simulated outputs of the order parameter, collectivity parameter, average fraction of walking, and within groups’ IQR of fraction of walking, respectively.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/1/eaav0695/DC1

    Fig. S1. Relation between the order and collectivity parameters.

    Fig. S2. Aggregation of walking and standing animals.

    Fig. S3. Distributions of the social-dependent probability to walk.

    Fig. S4. Global model results.

    Fig. S5. Local-fixed model results.

    Fig. S6. Local-dynamic model results.

    Fig. S7. Local grid-based model results.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Relation between the order and collectivity parameters.
    • Fig. S2. Aggregation of walking and standing animals.
    • Fig. S3. Distributions of the social-dependent probability to walk.
    • Fig. S4. Global model results.
    • Fig. S5. Local-fixed model results.
    • Fig. S6. Local-dynamic model results.
    • Fig. S7. Local grid-based model results.

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