Abstract: The ability of an animal to perform a task successfully is limited by the amount of attention being simultaneously focused on other activities. One way in which individuals might reduce the cost of divided attention is by preferentially focusing on the most beneficial tasks. In territorial animals where aggression is lower among familiar individuals, the decision to associate preferentially with familiar conspecifics may therefore confer advantages by allowing attention to be switched from aggression to predator vigilance and feeding. Wild juvenile brown trout were used to test the prediction that familiar fishes respond more quickly than unfamiliar fishes to a simulated predator attack. Our results confirm this prediction by demonstrating that familiar trout respond 14% faster than unfamiliar individuals to a predator attack. The results also show that familiar fishes consume a greater number of food items, foraging at more than twice the rate of unfamiliar conspecifics. To the best of our knowledge, these results provide the first evidence that familiarity–biased association confers advantages through the immediate fitness benefits afforded by faster predator–evasion responses and the long–term benefits provided by increased feeding opportunities.

Abstract: What are the neural bases of action understanding? Although this capacity could merely involve visual analysis of the action, it has been argued that we actually map this visual information onto its motor representation in our nervous system. Here we discuss evidence for the existence of a system, the ‘mirror system’, that seems to serve this mapping function in primates and humans, and explore its implications for the understanding and imitation of action.

Abstract: Complex networks are studied across many fields of science. To uncover their structural design principles, we defined “network motifs,” patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.

Abstract: In recent years, the concept of self-organization has been used to understand collective behaviour of animals. The central tenet of self-organization is that simple repeated interactions between individuals can produce complex adaptive patterns at the level of the group. Inspiration comes from patterns seen in physical systems, such as spiralling chemical waves, which arise without complexity at the level of the individual units of which the system is composed. The suggestion is that biological structures such as termite mounds, ant trail networks and even human crowds can be explained in terms of repeated interactions between the animals and their environment, without invoking individual complexity. Here, I review cases in which the self-organization approach has been successful in explaining collective behaviour of animal groups and societies. Ant pheromone trail networks, aggregation of cockroaches, the applause of opera audiences and the migration of fish schools have all been accurately described in terms of individuals following simple sets of rules. Unlike the simple units composing physical systems, however, animals are themselves complex entities, and other examples of collective behaviour, such as honey bee foraging with its myriad of dance signals and behavioural cues, cannot be fully understood in terms of simple individuals alone. I argue that the key to understanding collective behaviour lies in identifying the principles of the behavioural algorithms followed by individual animals and of how information flows between the animals. These principles, such as positive feedback, response thresholds and individual integrity, are repeatedly observed in very different animal societies. The future of collective behaviour research lies in classifying these principles, establishing the properties they produce at a group level and asking why they have evolved in so many different and distinct natural systems. Ultimately, this research could inform not only our understanding of animal societies, but also the principles by which we organize our own society.