Larger groups of passerines are more efficient problem solvers in the wild

Abstract

Group living commonly helps organisms face challenging environmental conditions. Although a known phenomenon in humans, recent findings suggest that a benefit of group living in animals generally might be increased innovative problem-solving efficiency. This benefit has never been demonstrated in a natural context, however, and the mechanisms underlying improved efficiency are largely unknown. We examined the problem-solving performance of great and blue tits at automated devices and found that efficiency increased with flock size. This relationship held when restricting the analysis to naive individuals, demonstrating that larger groups increased innovation efficiency. In addition to this effect of naive flock size, the presence of at least one experienced bird increased the frequency of solving, and larger flocks were more likely to contain experienced birds. These findings provide empirical evidence for the "pool of competence" hypothesis in nonhuman animals. The probability of success also differed consistently between individuals, a necessary condition for the pool of competence hypothesis. Solvers had a higher probability of success when foraging with a larger number of companions and when using devices located near rather than further from protective tree cover, suggesting a role for reduced predation risk on problem-solving efficiency. In contrast to traditional group living theory, individuals joining larger flocks benefited from a higher seed intake, suggesting that group living facilitated exploitation of a novel food source through improved problem-solving efficiency. Together our results suggest that both ecological and social factors, through reduced predation risk and increased pool of competence, mediate innovation in natural populations.

Fig. 1.

Residual proportion of devices solved (mean ± SEM) in relation to effective group sizes. Plotted values are residuals from a binomial GLMM of proportion of solved devices for group effort, with day nested within location as a random effect (n = 162, split into seven categories of 20 and one category of 22 data points each).

Fig. 2.

Residual proportion of devices solved (mean ± SEM) in relation to total group sizes in low and high perceived predation-risk positions (low risk was near cover, filled circles; high risk was far from cover, open circles). Plotted values are residuals from a binomial GLMM of proportion of solved devices for group effort, with trial nested within day and location as random effects (n = 324, split into seven categories of 20 and one category of 22 data points, allocated separately for data points near and far from cover).

Fig. 3.

Residual proportion of devices solved (mean ± SEM) by naive individuals in relation to the total number of naive individuals in the group, with linear fit shown. Plotted values are residuals from a binomial GLMM of proportion of solved devices for naive group effort, with day nested within location as a random effect (n = 161, split into seven categories of 20 and one category of 21 data points each; 1 trial out of the 162 had no attempt by naive individuals and was therefore excluded from the analysis).

Fig. 4.

Residual number of seeds per individual (mean ± SEM) against total group size. Plotted values are residuals from an LMM of the mean number of seeds per capita for group effort, with day nested within location as random effects (n = 162, split into seven categories of 20 and one category of 22 data points each).