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Collective animal behavior

define animal behavior, collective animal behavior
Collective animal behavior describes the coordinated behavior of large groups of similar animals as well as emergent properties of these groups This can include the costs and benefits of group membership, the transfer of information across the group, the group decision-making process, and group locomotion and synchronization Studying the principles of collective animal behavior has relevance to human engineering problems through the philosophy of biomimetics For instance, determining the rules by which an individual animal navigates relative to its neighbors in a group can lead to advances in the deployment and control of groups of swimming or flying micro-robots such as UAVs Unmanned Aerial Vehicles


  • 1 Examples
  • 2 Proposed functions
    • 21 Social interaction
    • 22 Protection from predators
    • 23 Enhanced foraging
    • 24 Increased locomotion efficiency
  • 3 Group structure
    • 31 Experimental approach
    • 32 Modeling approach
  • 4 Collective decision making
  • 5 See also
  • 6 References
  • 7 Recommended reading
  • 8 External links


Examples of collective animal behavior include:

  • Flocking birds
  • Herding ungulates
  • Shoaling and schooling fish
  • Swarming Antarctic krill
  • Pods of dolphins
  • Marching locusts
  • Nest building ants

Proposed functions

Many functions of animal aggregations have been proposed These proposed functions may be grouped into the four following categories:social and genetic, anti-predator, enhanced foraging, and increased locomotion efficiency

Social interaction

Support for the social and genetic function of aggregations, especially those formed by fish, can be seen in several aspects of their behavior For instance, experiments have shown that individual fish removed from a school will have a higher respiratory rate than those found in the school This effect has been partly attributed to stress, although hydrodynamic factors were considered more important in this particular study The calming effect of being with conspecifics may thus provide a social motivation for remaining in an aggregation Herring, for instance, will become very agitated if they are isolated from conspecifics Fish schools have also been proposed to serve a reproductive function since they provide increased access to potential mates

Protection from predators

School of goldband fusiliers

Several anti-predator functions of animal aggregations have been proposed One potential method by which fish schools or bird flocks may thwart predators is the ‘predator confusion effect’ proposed and demonstrated by Milinksi and Heller 1978 This theory is based on the idea that it becomes difficult for predators to pick out individual prey from groups because the many moving targets create a sensory overload of the predator's visual channel Milinski and Heller's findings have been corroborated both in experiment and computer simulations

A second potential anti-predator effect of animal aggregations is the "many eyes" hypothesis This theory states that as the size of the group increases, the task of scanning the environment for predators can be spread out over many individuals Not only does this mass collaboration presumably provide a higher level of vigilance, it could also allow more time for individual feeding

A third hypothesis for an anti-predatory effect of animal aggregation is the "encounter dilution" effect Hamilton, for instance, proposed that the aggregation of animals was due to a "selfish" avoidance of a predator and was thus a form of cover-seeking Another formulation of the theory was given by Turner and Pitcher and was viewed as a combination of detection and attack probabilities In the detection component of the theory, it was suggested that potential prey might benefit by living together since a predator is less likely to chance upon a single group than a scattered distribution In the attack component, it was thought that an attacking predator is less likely to eat a particular animal when a greater number of individuals are present In sum, an individual has an advantage if it is in the larger of two groups, assuming that the probability of detection and attack does not increase disproportionately with the size of the group

Enhanced foraging

A third proposed benefit of animal groups is that of enhanced foraging This ability was demonstrated by Pitcher and others in their study of foraging behavior in shoaling cyprinids In this study, the time it took for groups of minnows and goldfish to find a patch of food was quantified The number of fishes in the groups was varied, and a statistically significant decrease in the amount of time necessary for larger groups to find food was established Further support for an enhanced foraging capability of schools is seen in the structure of schools of predatory fish Partridge and others analyzed the school structure of Atlantic bluefin tuna from aerial photographs and found that the school assumed a parabolic shape, a fact that was suggestive of cooperative hunting in this species Partridge et al, 1983

Increased locomotion efficiency

This theory states that groups of animals moving in a fluid environment may save energy when swimming or flying together, much in the way that bicyclists may draft one another in a peloton Geese flying in a Vee formation are also thought to save energy by flying in the updraft of the wingtip vortex generated by the previous animal in the formation Ducklings have also been shown to save energy by swimming in a line Increased efficiencies in swimming in groups have also been proposed for schools of fish and Antarctic krill

Group structure

The structure of large animal groups has been difficult to study because of the large number of animals involved The experimental approach is therefore often complemented by mathematical modeling of animal aggregations

Experimental approach

Experiments investigating the structure of animal aggregations seek to determine the 3D position of each animal within a volume at each point in time It is important to know the internal structure of the group because that structure can be related to the proposed motivations for animal grouping This capability requires the use of multiple cameras trained on the same volume in space, a technique known as stereophotogrammetry When hundreds or thousands of animals occupy the study volume, it becomes difficult to identify each one In addition, animals may block one another in the camera views, a problem known as occlusion Once the location of each animal at each point in time is known, various parameters describing the animal group can be extracted

These parameters include:

Density:The density of an animal aggregation is the number of animals divided by the volume or area occupied by the aggregation Density may not be a constant throughout the group For instance, starling flocks have been shown to maintain higher densities on the edges than in the middle of the flock, a feature that is presumably related to defense from predators

Polarity:The group polarity describes if the group animals are all pointing in the same direction or not In order to determine this parameter, the average orientation of all animals in the group is determined For each animal, the angular difference between its orientation and the group orientation is then found The group polarity is then the average of these differences Viscido 2004

Nearest Neighbor Distance:The nearest neighbor distance NND describes the distance between the centroid of one animal the focal animal and the centroid of the animal nearest to the focal animal This parameter can be found for each animal in an aggregation and then averaged Care must be taken to account for the animals located at the edge of an animal aggregation These animals have no neighbor in one direction

Nearest Neighbor Position:In a polar coordinate system, the nearest neighbor position describes the angle and distance of the nearest neighbor to a focal animal

Packing Fraction:Packing fraction is a parameter borrowed from physics to define the organization or state ie solid, liquid, or gas of 3D animal groups It is an alternative measure to density In this parameter, the aggregation is idealized as an ensemble of solid spheres, with each animal at the center of a sphere The packing fraction is defined as the ratio of the total volume occupied by all individual spheres divided by the global volume of the aggregation Cavagna 2008 Values range from zero to one, where a small packing fraction represents a dilute system like a gas Cavagna found that the packing fraction for groups of starlings was 0012

Integrated Conditional Density:This parameter measures the density at various length scales and therefore describes the homogeneity of density throughout an animal group

Pair Distribution Function:This parameter is usually used in physics to characterize the degree of spatial order in a system of particles It also describes the density, but this measures describes the density at a distance away from a given point Cavagna et al found that flocks of starlings exhibited more structure than a gas but less than a liquid

Modeling approach

The simplest mathematical models of animal aggregations generally instruct the individual animals to follow three rules:

  1. Move in the same direction as your neighbor
  2. Remain close to your neighbors
  3. Avoid collisions with your neighbors
A diagram illustrating the difference between 'metric distance' and 'topological distance' in reference to fish schools

An example of such a simulation is the Boids program created by Craig Reynolds in 1986 Another is the Self Propelled Particle model Many current models use variations on these rules For instance, many models implement these three rules through layered zones around each animal In the zone of repulsion very close to the animal, the focal animal will seek to distance itself from its neighbors in order to avoid a collision In the slightly further away zone of alignment, a focal animal will seek to align its direction of motion with its neighbors In the outmost zone of attraction, which extends as far away from the focal animal as it is able to sense, the focal animal will seeks to move towards a neighbor The shape of these zones will necessarily be affected by the sensory capabilities of the animal For example, the visual field of a bird does not extend behind its body Fish, on the other hand, rely on both vision and on hydrodynamic signals relayed through its lateral line Antarctic krill rely on vision and on hydrodynamic signals relayed through its antennae

Recent studies of starling flocks have shown, however, that each bird modifies its position relative to the six or seven animals directly surrounding it, no matter how close or how far away those animals are Interactions between flocking starlings are thus based on a topological rule rather than a metric rule It remains to be seen whether the same rule can be applied to other animals Another recent study, based on an analysis of high speed camera footage of flocks above Rome and assuming minimal behavioural rules, has convincingly simulated a number of aspects of flock behaviour

Collective decision making

Aggregations of animals are faced with decisions which they must make if they are to remain together For a school of fish, an example of a typical decision might be which direction to swim when confronted by a predator Social insects such as ants and bees must collectively decide where to build a new nest A herd of elephants must decide when and where to migrate How are these decisions made Do stronger or more experienced 'leaders' exert more influence than other group members, or does the group make a decision by consensus The answer probably depends on the species While the role of a leading matriarch in an elephant herd is well known, studies have shown that some animal species use a consensus approach in their collective decision-making process

A recent investigation showed that small groups of fish used consensus decision-making when deciding which fish model to follow The fish did this by a simple quorum rule such that individuals watched the decisions of others before making their own decisions This technique generally resulted in the 'correct' decision but occasionally cascaded into the 'incorrect' decision In addition, as the group size increased, the fish made more accurate decisions in following the more attractive fish model Consensus decision-making, a form of collective intelligence, thus effectively uses information from multiple sources to generally reach the correct conclusion

Some simulations of collective decision-making use the Condorcet method to model the way groups of animals come to consensus

See also

  • Biomimetics
  • Collective intelligence
  • Swarm intelligence
  • Emergence


  1. ^ Abrahams, M; Colgan, P 1985 "Risk of predation, hydrodynamic efficiency, and their influence on school structure" Environmental Biol of Fishes 13 3:195–202 doi:101007/bf00000931 
  2. ^ Partridge, B; Pitcher, T; Cullen, M; Wilson, J 1980 "The three-dimensional structure of fish schools" Behav Ecol and Sociobiology 6 4:277–288 doi:101007/bf00292770 
  3. ^ Milinski, H; Heller, R 1978 "Influence of a predator on the optimal foraging behavior of sticklebacks" Nature 275:642–644 doi:101038/275642a0 
  4. ^ Jeschke JM, Tollrian R 2007 "Prey swarming:which predators become confused and why" Animal Behaviour 74 3:387–393 doi:101016/janbehav200608020 
  5. ^ Ioannou CC, Tosh CR, Neville L, Krause J 2008 "The confusion effect—from neural networks to reduced predation risk" Behavioral Ecology 19 1:126–130 doi:101093/beheco/arm109 
  6. ^ Krakauer DC 1995 "Groups confuse predators by exploiting perceptual bottlenecks:a connectionist model of the confusion effect" Behavioral Ecology and Sociobiology 36 6:421–429 doi:101007/BF00177338 
  7. ^ Olson RS, Hintze A, Dyer FC, Knoester DB, Adami C 2013 "Predator confusion is sufficient to evolve swarming behaviour" J R Soc Interface 10 85:20130305 doi:101098/rsif20130305 PMID 23740485 
  8. ^ Demsar J, Hemelrijk CK, Hildenbrandt H, Bajec IL 2015 "Simulating predator attacks on schools:Evolving composite tactics" Ecological Modelling 304:22–33 doi:101016/jecolmodel201502018 
  9. ^ Roberts, G 1996 "Why individual vigilance declines as group size increases" Anim Behav 51:1077–1086 doi:101006/anbe19960109 
  10. ^ Lima, S 1995 "Back to the basics of anti-predatory vigilance:the group-size effect" Animal Behaviour 49 1:11–20 doi:101016/0003-34729580149-9 
  11. ^ Olson RS, Haley PB, Dyer FC, Adami C 2015 "Exploring the evolution of a trade-off between vigilance and foraging in group-living organisms" Royal Society Open Science 2:150135 doi:101098/rsos150135 
  12. ^ Hamilton, W D 1971 "Geometry for the selfish herd" J Theor Biology 31 2:295–311 doi:101016/0022-51937190189-5 PMID 5104951 
  13. ^ Olson RS, Knoester DB, Adami C 2013 "Critical Interplay Between Density-dependent Predation and Evolution of the Selfish Herd" Proceedings of GECCO 2013:247–254 doi:101145/24633722463394 
  14. ^ Turner, G; Pitcher, T 1986 "Attack abatement:a model for group protection by combined avoidance and dilution" American Naturalist 128 2:228–240 doi:101086/284556 
  15. ^ Krause, J; Ruxton, G; Rubenstein, D 1998 "Is there always an influence of shoal size on predator hunting success" Journal of Fish Biology 52:494–501 doi:101006/jfbi19970595 
  16. ^ Pitcher, T; Magurran, A; Winfield, I 1982 "Fish in larger shoals find food faster" Behav Ecol and Sociobiology 10:2 
  17. ^ Partridge, B Johansson; Kalish, J 1983 "The structure of schools of giant bluefin tuna in Cape Cod Bay" Environmental Biology of Fishes 9 3-4:253–262 doi:101007/bf00692374 
  18. ^ Fish, FE Kinematics of ducklings swimming in formation:consequences of position 1995 Journal of Experimental Zoology 273:1-11
  19. ^ M Ballerini, N Cabibbo, R Candelier, A Cavagna, E Cisbani, I Giardina, A Orlandi, G Parisi, A Procaccini, M Viale, and V Zdravkovic 2008 ‘Empirical investigation of starling flocks:a benchmark study in collective animal behaviour’ Animal Behavior 761:201-215
  20. ^ Viscido, S; Parrish, J; Grunbaum, D 2004 "Individual behavior and emergent properties of fish schools:a comparison of observation and theory" Marine Ecology Progress Series 273:239–249 doi:103354/meps273239 
  21. ^ a b c Cavagna, A; Cimarelli, Giardina; Orlandi, Parisi; Procaccini, Santagati; Stefanini 2008 "New statistical tools for analyzing the structure of animal groups" Mathematical Biosciences 214:32–37 doi:101016/jmbs200805006 
  22. ^ M Ballerini, N Cabibbo, R Candelier, A Cavagna, E Cisbani, I Giardina, V Lecomte, A Orlandi, G Parisi, A Procaccini, M Viale, and V Zdravkovic 2008 ‘Interaction ruling animal collective behavior depends on topological rather than metric distance:Evidence from a field study’ PNAS 105:1232-1237
  23. ^ Hildenbrandt, H; Carere, C; Hemelrijk, CK 2010 "Self-organized aerial displays of thousands of starlings:a model" Behavioral Ecology 21 6:1349–1359 doi:101093/beheco/arq149 
  24. ^ Hemelrijk, CK; Hildenbrandt, H 2011 "Some causes of the variable shape of flocks of birds" PLOS ONE 6 8:e22479 doi:101371/journalpone0022479 
  25. ^ Project Starflag
  26. ^ Swarm behaviour model by University of Groningen
  27. ^ Sumpter, D http://wwwcollective-behaviorcom/indexhtml
  28. ^ Sumpter, D; Krause, J; James, R; Couzin, I; Ward, A 2008 "Consensus decision making by fish" Current Biology 18 22:1773–1777 doi:101016/jcub200809064 PMID 19013067 

Recommended reading

Camazine, S, Deneubourg, JL, Franks, NR, Sneyd, J, Theraulaz, G and Bonabeau, E 2001 Self-Organization in Biological Systems Princeton University Press, Princeton, NJ ISBN 0-691-01211-3 especially Chapter 11

Sumpter, D J T 2010 "Collective Animal Behavior" Princeton University Press, Princeton, ISBN 978-0-691-14843-4

External links

  • Collective Animal Behavior website organized around David Sumpter's book 2008 by the same name
  • STARFLAG project:Description of starling flocking project
  • Center for Biologically Inspired Design at Georgia Tech
  • David Sumpter's research website
  • Iain Couzin's research website
  • Website of Julia Parrish, an animal aggregation researcher
  • Research for this Wikipedia entry was conducted as a part of a Locomotion Neuromechanics course APPH 6232 offered in the School of Applied Physiology at Georgia Tech

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