Effective leadership and decision-making in animal groups on the move

Abstract

For animals that forage or travel in groups, making movement decisions often depends on social interactions among group members 1,2 . However, in many cases, few individuals have pertinent information, such as knowledge about the location of a food source 3,4 , or of a migration route [5][6][7][8][9] . Using a simple model we show how information can be transferred within groups both without signalling and when group members do not know which individuals, if any, have information. We reveal that the larger the group the smaller the proportion of informed individuals needed to guide the group, and that only a very small proportion of informed individuals is required to achieve great accuracy. We also demonstrate how groups can make consensus decisions, even though informed individuals do not know whether they are in a majority or minority, how the quality of their information compares with that of others, or even whether there are any other informed individuals. Our model provides new insights into the mechanisms of effective leadership and decision-making in biological systems.Primary questions concerning the mechanism of information transfer in groups include how uninformed individuals recognize those that are informed, whether such recognition is actually necessary, and how groups can come to a collective decision when informed individuals differ in preference 10 . It is known that several animal species have evolved specific recruitment signals that help guide conspecifics. Most famous in this context is the waggle-dance of the honeybee that recruits hive members to visit food sources 5,7,8,11 . Furthermore, valuable experience may be correlated with age or dominance 1,2 , which can presumably be estimated by conspecifics of some species 12 . However, it remains questionable whether such explanations hold when migrating groups of fish, ungulates, insects and birds are considered, where crowding limits the range over which individuals can detect one another 1,2 . In pelagic fish schools, for example, individuals are usually less than one body-length apart 13 . Although it is likely that some species have a genetically determined propensity to migrate in a general direction 14,15 , or respond to abiotic cues such as thermal gradients that may aid migration 16,17 , it is likely for many species that experienced group members play an important role in guiding those that are less experienced or inexperienced. Relatively few informed individuals within fish schools are known to be able to influence the foraging behaviour of the group 3 and the ability of a school to navigate towards a target 4 . Similarly, very few individuals (approximately 5%) within honeybee swarms can guide the group to a new nest site 7 .Furthermore, for some animal groups such as large insect swarms or fish schools, it may be unreasonable to assume that group members have the capacity for individual recognition. Here we address two fundamental issues, both occurring in the absence of complex signalling mechanisms and when it is not possible f...

Science, technology and the future of small autonomous drones

“…Computational models of bird flocking build on the combination of three simple behavioural rules, namely repulsion from near individuals, movement towards far neighbours and alignment with average velocities of neighbours 76 . Remarkably, these rules can also account for hierarchical flocking without explicit signalling of experienced agents 77 . However, perceiving the position of other flying drones is still very challenging.…”

Place versus response learning in fish: a comparison between species

“…Further experiments using 475 shoals of zebrafish should be completed to assess whether navigation is 476 facilitated by collective decision-making in this species (Couzin et al 2005). 477…”

Heterogeneous animal group models and their group-level alignment dynamics: An equation-free approach

“…A long-range interaction has been incorporated into the SPP model (Mikhailov and Zanette, 1999), and continuum, "hydrodynamic" versions of this model have been introduced Tu, 1995, 1998;Topaz et al, 2006). Recently, Couzin et al (2002Couzin et al ( , 2005) have introduced a model to provide insights into the mechanism of decision making in biological systems, which reproduces many important observations made in the field, and provides new insights into these phenomena. A review for various models can be found in Parrish et al (2002) and Czirók and Vicsek (2001).…”