Lifespan behavioural and neural resilience in a social insect

Analyses of senescence in social species are important to understanding how group living influences the evolution of ageing in society members. Social insects exhibit remarkable lifespan polyphenisms and division of labour, presenting excellent opportunities to test hypotheses concerning ageing and behaviour. Senescence patterns in other taxa suggest that behavioural per- formance in ageing workers would decrease in association with declining brain functions. Using the ant Pheidole dentata as a model, we found that 120-day-old minor workers, having completed 86% of their laboratory lifespan, showed no decrease in sensorimotor functions underscoring complex tasks such as alloparenting and foraging. Collaterally, we found no age-associated increases in apoptosis in functionally specialized brain compartments or decreases in synaptic densities in the mushroom bodies, regions associa- ted with integrative processing. Furthermore, brain titres of serotonin and dopamine—neuromodulators that could negatively impact behaviour through age-related declines—increased in old workers. Unimpaired task performance appears to be based on the maintenance of brain functions supporting olfaction and motor coordination independent of age. Our study is the first to comprehensively assess lifespan task performance and its neurobiological correlates and identify constancy in behavioural performance and the absence of significant age-related neural declines

Characterization of the walking behaviour during the control test.

<p>(A) The average walking speed of all participants as they were walking alone in the experimental corridor. The grey area indicates the standard deviation of the mean. The dashed lines are the limits of the measurement zone, where the pedestrians are assumed to have reached their comfortable walking speed. (B) The comfortable walking speeds are normally distributed with mean  = 1.2 m/s and standard deviation  = 0.16 (a Kolmogorov-Smirnov test yields a p-value of 0.73).</p

Illustration of the unstable dynamics observed under experimental conditions for one replication with N = 60 pedestrians.

<p>Illustration of the unstable dynamics observed under experimental conditions for one replication with N = 60 pedestrians.</p

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Illustration of the clustering method.

<p>(A) Two pedestrians <i>i</i> and <i>j</i> belong to the same cluster if one follows the other. (B) The pedestrian <i>j</i> follows pedestrian <i>i</i>, if <i>j</i> moves closer than a distance from the position of pedestrian <i>i</i> at time <i>t</i>, during a time period of seconds. Here,  = 1 s and  = 0.6 m are two clustering parameters.</p

Correlation between local radial speed and density gaps.

<p>(A) Local density maps for three representative replications with N = 30, 50 and 60 pedestrians. The emergence and the propagation of density peaks (red) and density gaps (blue) are visible. (B) Local radial speed for the same three replications, showing the lateral movements of pedestrians. The largest values occur mostly around density gaps. (C) Average local density as a function of local radial speed, for all replications with N = 30, 50 and 60 pedestrians. The largest values of occur where the local density level is low, that is, around density gaps. This correlation is less visible for N = 30, probably due to the lower global density level.</p

(A) Illustration of the evolution of the number of clusters for three replications with N = 30, 50 and 60 pedestrians.

<p>The clustering method is described in the <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002442#s4" target="_blank">Materials and Methods</a> section and illustrated <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002442#pcbi-1002442-g002" target="_blank">Fig. 2</a>. During the first ten seconds, the initial transition from disorder to order is visible. Then, the number of clusters oscillates between well-organized (five clusters or less), and disorganized states (ten clusters or more). (B) The corresponding segregation dynamics for the same three replications.</p

Collective dynamics predicted in simulations.

<p>(A) Cluster lifetime as a function of the standard deviation of the comfortable walking speed distribution, as predicted by numerical simulations. The decreasing curves demonstrate the relationship between inter-individual variability and traffic instabilities. The width of the curves indicates the 95% confidence bounds of the lifetime estimation. (B) The collective payoff provided by the lane organization, as a function of . (C) The individual payoff of pedestrians averaged over all simulations for N = 30, N = 50, and N = 60, grouped according to their desired walking speed. The black areas indicate the absence of value.</p

Empirical distribution of the clusters lifetime.

<p>(A) The probability for a cluster to remain unchanged after a time period of <i>t</i> seconds. (B) <i>log(p)</i> versus <i>t</i> does not yield a straight line, showing that <i>p(t)</i> decays slower than an exponential. (C) <i>log(p)</i> versus <i>log(t)</i> is a curve, showing that <i>p(t)</i> decays faster than a power-law. (D) A straight line is found for <i>log(p)</i> versus <i>t^k</i> with <i>k = 0.4</i>, demonstrating that the lifetime of pedestrian clusters follows a stretched exponential relaxation law: , where the relaxation exponent <i>k</i> depends on the number of pedestrians N. The insets indicate simulation results, where the same distribution law is found. Empirical data and computer simulations yield the same relaxation exponents <i>k</i> = 0.6, 0.5, and 0.5 for N = 30, 50 and 60 respectively.</p

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