Selfish herd theory

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The selfish herd theory states that individuals within a population attempt to reduce their predation risk by putting other conspecifics between themselves and predators. [1] A key element in the theory is the domain of danger, the area of ground in which every point is nearer to a particular individual than to any other individual. Such antipredator behavior inevitably results in aggregations. The theory was proposed by W. D. Hamilton in 1971 to explain the gregarious behavior of a variety of animals. [1] It contrasted the popular hypothesis that evolution of such social behavior was based on mutual benefits to the population. [1]

Contents

The basic principle governing selfish herd theory is that in aggregations, predation risk is greatest on the periphery and decreases toward the center. [1] More dominant animals within the population are proposed to obtain low-risk central positions, whereas subordinate animals are forced into higher risk positions. [2] The hypothesis has been used to explain why populations at higher predation risk often form larger, more compact groups. [3] It may also explain why these aggregations are often sorted by phenotypic characteristics such as strength. [4]

Hamilton's selfish herd

In a group, in W. D. Hamilton's theory, prey seek central positions in order to reduce their domain of danger. Individuals along the outer edges of the group are more at risk of being targeted by the predator. Domain of Danger Graphic.jpg
In a group, in W. D. Hamilton's theory, prey seek central positions in order to reduce their domain of danger. Individuals along the outer edges of the group are more at risk of being targeted by the predator.

W. D. Hamilton proposed the theory in an article titled "Geometry for the Selfish Herd". To date, this article has been cited in over 2000 sources. To illustrate his theory, Hamilton asked readers to imagine a circular lily pond which sheltered a population of frogs and a water snake. [1] Upon seeing the water snake, the frogs scatter to the rim of the pond, and the water snake attacks the nearest one. [1] Hamilton proposed that in this model, each frog had a better chance of not being closest to, and thus vulnerable to attack by, the water snake if he was between other frogs. [1] As a result, modeled frogs jumped to smaller gaps between neighboring frogs. [1]

Domain of danger

This simple example was based on what Hamilton identified as each frog's domain of danger, the area of ground in which any point was nearer to that individual than it was to any other individual. [1] The model assumed that frogs were attacked from random points and that if an attack was initiated from within an individual's domain of danger, he would be attacked and likely killed. The risk of predation to each individual was, therefore, correlated to the size of his domain of danger. [1] Frog jumping in response to the water snake was an attempt to lower the domain of danger. [1]

Domains of danger shown by a Voronoi diagram of non-herd individuals. Voronoi growth euclidean.gif
Domains of danger shown by a Voronoi diagram of non-herd individuals.

Hamilton also went on to model predation in two-dimensions, using a lion as an example. Movements that Hamilton proposed would lower an individual's domain of danger were largely based on the theory of marginal predation. This theory states that predators attack the closest prey, who are typically on the outside of an aggregation. [1] From this, Hamilton suggested that in the face of predation, there should be a strong movement of individuals toward the center of an aggregation. [1]

A domain of danger may be measured by constructing a Voronoi diagram around the group members. [5] Such construction forms a series of convex polygons surrounding each individual in which all points within the polygon are closer to that individual than to any other. [5]

Movement rules

Movements toward the center of an aggregation are based upon a variety of movement rules that range in complexity. [3] Identifying these rules has been considered the "dilemma of the selfish herd". [6] The main issue is that movement rules that are easy to follow are often unsuccessful in forming compact aggregations, and those that do form such aggregations are often considered too complex to be biologically relevant. [6] Viscido, Miller, and Wethey identified three factors that govern good movement rules. [6] According to such factors, a plausible movement rule should be statistically likely to benefit its followers, should be likely to fit the capabilities of an animal, and should result in a compact aggregation with desired central movement. [6] Identified movement rules include:

Nearest Neighbor Rule
This rule states that individuals within a population move towards their nearest neighbor. [1] It is the mechanism originally proposed by Hamilton. This rule, however, may not be beneficial in small aggregations, where moving toward nearest neighbor does not necessarily correlate to movement from the periphery.
Time Minimization Rule
This rule states that individuals within a population move toward their nearest neighbor in time. [7] This rule has gained popularity as it considers the biological constraints of an animal, as well as its orientation in space. [7]
Local Crowded Horizon Rule
This rule states that individuals within a population consider the location of many, if not all, other members within the population in guiding their movements. [6]

Research has revealed a variety of factors that may influence chosen movement rules. These factors include initial spatial position, [3] population density, [3] attack strategy of the predator, [3] and vigilance. [8] Individuals holding initially central positions are more likely to be successful at remaining in the center. [3] Simpler movement strategies may be sufficient for low density populations and fast-acting predators, but at higher densities and with slower predators, more complex strategies may be needed. [3] Lastly, less vigilant members of a herd are often less likely to obtain smaller domains of danger as they begin movement later. [8]

Escape-route strategies

The selfish herd theory may also be applied to the group escape of prey in which the safest position, relative to predation risk, is not the central position, but rather the front of the herd. [2] The theory may be useful in explaining the escape strategy chosen by a herd leader. [2] Members at the back of the herd have the greatest domain of danger and suffer the highest predation risk. These slow members must choose whether to stay in the herd, and thus be the most likely targets, or whether to desert the herd, and signal their vulnerability. The latter may entice the pursuit of the predator to this sole individual. In light of this, the decision of the escape route by the front members of the herd may be greatly affected by actions of the slowest members. [2] If the leader chooses an escape strategy that promotes the dispersal of the slowest member of the herd, he may endanger himself—causing dissipation of his protective buffer. Five types of herd leadership have been proposed based on the decisions of the leader:

Although some types of escape are seemingly altruistic, they promote the stability of the herd, and thus decrease the predation risk of the leader. This choice is often affected by the terrain of the area. [2]

Evolution

Gregarious behavior occurs in a wide variety of taxa and thus, has likely evolved independently on several occasions. [9] Dilution of predation risk is one of many proposed benefits that have facilitated the selection of such behavior. Much research has been devoted to understanding the possible evolution of the selfish herd and thus, the plausibility of the theory. In order for the selfish herd to have evolved, movement rules that decreased domains of danger within a population must have been selected. [9] Because such rules are often complex, it is unlikely that they would have evolved in a single step. [9] Rather, simple rules that considered solely the nearest neighbor in guiding movement may have given rise to the evolution of more complicated rules. [9] This proposed succession would only occur if individuals who moved toward their nearest neighbor in the face of predation showed a higher survival than those who did not. Furthermore, individuals must have benefited from such movements more often than they were harmed (i.e. forced onto the periphery and attacked). [9] This idea has, in fact, gained support. [10] A study conducted by Reluga and Viscido found that natural selection of localized movement rules of members within a population could, in fact, promote the evolution of the selfish herd. [9] Further, it has been shown that how the predator attacks plays a crucial role in whether or not selfish herd behavior can evolve. [11]

Trade-offs

Although the selfish herd promotes decreased predation risk to many of its members, a variety of risks have been associated with such aggregations. Groupings may make prey more conspicuous to predators [3] and may increase intraspecific competition. Furthermore, individuals in the desired central positions may have lower feeding rates [3] and may be less vigilant. [8]

Examples

An extensively studied example is the fiddler crab. When exposed to a predator, fiddler crabs move in ways that are consistent with the selfish herd theory. [5] Dispersed groups are more likely to form an aggregate when subjected to danger and crabs attempt to run toward the center of a forming group. [12]

Selfish herd behavior is seen also in:

Limitations

Although the selfish herd theory is widely accepted, it has been deemed implausible in certain situations. It may not fully account for aggregations in 3-dimensional space, in which predatory attacks may come from above or below. [3] This means that the grouping behavior of flying birds and some aquatic animals is unlikely to be explained by the selfish herd theory. The theory may require complex movement rules that are too difficult for an animal to follow. [10] Other mechanisms have been proposed to better explain the grouping behavior of animals, such as the confusion hypothesis. Research has indicated that this hypothesis is more likely in small groups (2-7 members), however, and that further increasing group size has little effect. [19]

Related Research Articles

<span class="mw-page-title-main">Theoretical ecology</span>

Theoretical ecology is the scientific discipline devoted to the study of ecological systems using theoretical methods such as simple conceptual models, mathematical models, computational simulations, and advanced data analysis. Effective models improve understanding of the natural world by revealing how the dynamics of species populations are often based on fundamental biological conditions and processes. Further, the field aims to unify a diverse range of empirical observations by assuming that common, mechanistic processes generate observable phenomena across species and ecological environments. Based on biologically realistic assumptions, theoretical ecologists are able to uncover novel, non-intuitive insights about natural processes. Theoretical results are often verified by empirical and observational studies, revealing the power of theoretical methods in both predicting and understanding the noisy, diverse biological world.

<span class="mw-page-title-main">Predation</span> Biological interaction

Predation is a biological interaction where one organism, the predator, kills and eats another organism, its prey. It is one of a family of common feeding behaviours that includes parasitism and micropredation and parasitoidism. It is distinct from scavenging on dead prey, though many predators also scavenge; it overlaps with herbivory, as seed predators and destructive frugivores are predators.

<span class="mw-page-title-main">Group selection</span> Proposed mechanism of evolution

Group selection is a proposed mechanism of evolution in which natural selection acts at the level of the group, instead of at the level of the individual or gene.

<span class="mw-page-title-main">Foraging</span> Searching for wild food resources

Foraging is searching for wild food resources. It affects an animal's fitness because it plays an important role in an animal's ability to survive and reproduce. Foraging theory is a branch of behavioral ecology that studies the foraging behavior of animals in response to the environment where the animal lives.

<span class="mw-page-title-main">Common minnow</span> Species of fish

The Eurasian minnow, minnow, or common minnow is a small species of freshwater fish in the carp family Cyprinidae. It is the type species of genus Phoxinus. It is ubiquitous throughout much of Eurasia, from Britain and Spain to eastern Siberia, predominantly in cool streams and well-oxygenated lakes and ponds. It is noted for being a gregarious species, shoaling in large numbers.

<span class="mw-page-title-main">Safety in numbers</span> Hypothesis

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<span class="mw-page-title-main">Herd</span> Similar as Group

A herd is a social group of certain animals of the same species, either wild or domestic. The form of collective animal behavior associated with this is called herding. These animals are known as gregarious animals.

<span class="mw-page-title-main">Anti-predator adaptation</span> Defensive feature of prey for selective advantage

Anti-predator adaptations are mechanisms developed through evolution that assist prey organisms in their constant struggle against predators. Throughout the animal kingdom, adaptations have evolved for every stage of this struggle, namely by avoiding detection, warding off attack, fighting back, or escaping when caught.

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In 1938, the Austrian ethologist Karl von Frisch made his first report on the existence of the chemical alarm signal known as Schreckstoff in minnows. An alarm signal is a response produced by an individual, the "sender", reacting to a hazard that warns other animals, the receivers, of danger. This chemical alarm signal is released only when the sender incurs mechanical damage, such as when it has been caught by a predator, and is detected by the olfactory system. When this signal reaches the receivers, they perceive a greater predation risk and exhibit an antipredator response. Since populations of fish exhibiting this trait survive more successfully, the trait is maintained via natural selection. While the evolution of this signal was once a topic of great debate, recent evidence suggests schreckstoff evolved as a defense against environmental stressors such as pathogens, parasites, and UVB radiation and that it was later co-opted by predators and prey as a chemical signal.

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<span class="mw-page-title-main">Altruism (biology)</span> Behaviour that increases the fitness of another while decreasing the fitness of self

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<span class="mw-page-title-main">Flock (birds)</span> A group of individual birds travelling together

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<span class="mw-page-title-main">Mobbing (animal behavior)</span> Antipredator adaptation in which individuals of prey species cooperatively attack a predator

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Spatial organization can be observed when components of an abiotic or biological group are arranged non-randomly in space. Abiotic patterns, such as the ripple formations in sand dunes or the oscillating wave patterns of the Belousov–Zhabotinsky reaction emerge after thousands of particles interact millions of times. On the other hand, individuals in biological groups may be arranged non-randomly due to selfish behavior, dominance interactions, or cooperative behavior. W. D. Hamilton (1971) proposed that in a non-related "herd" of animals, the spatial organization is likely a result of the selfish interests of individuals trying to acquire food or avoid predation. On the other hand, spatial arrangements have also been observed among highly related members of eusocial groups, suggesting that the arrangement of individuals may provide advantages for the group.

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<span class="mw-page-title-main">Pursuit predation</span> Hunting strategy by some predators

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References

  1. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Hamilton, W.D. (1971). "Geometry for the Selfish Herd". Journal of Theoretical Biology. 31 (2): 295–311. Bibcode:1971JThBi..31..295H. doi:10.1016/0022-5193(71)90189-5. PMID   5104951.
  2. 1 2 3 4 5 Eshel, Ilan; Sansone, Emilia; Shaked, Avner (2011). "On the evolution of group-escape strategies of selfish prey". Theoretical Population Biology. 80 (2h): 150–157. doi:10.1016/j.tpb.2011.06.005. PMID   21712051.
  3. 1 2 3 4 5 6 7 8 9 10 Morrell, L. J.; Ruxton, G. D.; James, R. (2010). "Spatial positioning in the selfish herd". Behavioral Ecology. 22 (1): 16–22. doi: 10.1093/beheco/arq157 .
  4. ↑ Croft, D.P.; Darden, S.K.; Ruxton, G.D. (2009). "Predation risk as a driving force for phenotypic assortment: a cross-population comparison". Proceedings of the Royal Society B: Biological Sciences. 276 (1663): 1899–1904. doi:10.1098/rspb.2008.1928. PMC   2674500 . PMID   19324770.
  5. 1 2 3 Viscido, Steven V.; Miller, Matthew; Wethey, David S. (2001). "The Response of a Selfish Herd to an Attack from Outside the Group Perimeter". Journal of Theoretical Biology. 208 (3): 315–328. Bibcode:2001JThBi.208..315V. doi:10.1006/jtbi.2000.2221. PMID   11207093.
  6. 1 2 3 4 5 Viscido, Steven V.; Miller, Matthew; Wethey, David S. (2002). "The Dilemma of the Selfish Herd: The Search for a Realistic Movement Rule". Journal of Theoretical Biology. 217 (2): 183–194. Bibcode:2002JThBi.217..183V. doi:10.1006/jtbi.2002.3025. PMID   12202112.
  7. 1 2 James, R.; Bennett, P.G.; Krause, J. (2004). "Geometry for mutualistic and selfish herds: the limited domain of danger". Journal of Theoretical Biology. 228 (1): 107–113. Bibcode:2004JThBi.228..107J. doi:10.1016/j.jtbi.2003.12.005. PMID   15064086.
  8. 1 2 3 Beauchamp, Guy (1 March 2007). "Vigilance in a selfish herd". Animal Behaviour. 73 (3): 445–451. doi:10.1016/j.anbehav.2006.09.004. S2CID   53166269.
  9. 1 2 3 4 5 6 Reluga, Timothy C.; Viscido, Steven (2005). "Simulated evolution of selfish herd behavior". Journal of Theoretical Biology. 234 (2): 213–225. Bibcode:2005JThBi.234..213R. doi:10.1016/j.jtbi.2004.11.035. PMID   15757680.
  10. 1 2 Morton, Thomas L.; Haefner, James W.; Nugala, Vasudevarao; Decino, Robert D.; Mendes, Lloyd (1994). "The selfish herd revisited: Do simple movement rules reduce relative predation risk?". Journal of Theoretical Biology. 167 (1): 73–79. Bibcode:1994JThBi.167...73M. doi:10.1006/jtbi.1994.1051.
  11. ↑ Olson RS; Knoester DB; Adami C (2013). "Critical interplay between density-dependent predation and evolution of the selfish herd". Proceedings of the 15th annual conference on Genetic and evolutionary computation. Gecco '13. pp. 247–254. doi:10.1145/2463372.2463394. ISBN   9781450319638. S2CID   14414033.{{cite book}}: CS1 maint: date and year (link)
  12. ↑ Viscido, Steven V.; Wethey, David S. (2002). "Quantitative analysis of fiddler crab flock movement: evidence for 'selfish herd' behaviour". Animal Behaviour. 63 (4): 735–741. doi:10.1006/anbe.2001.1935. S2CID   53198241.
  13. ↑ Orpwood, James E.; Magurran, Anne E.; Armstrong, John D.; Griffiths, Siân W. (2008). "Minnows and the selfish herd: effects of predation risk on shoaling behaviour are dependent on habitat complexity". Animal Behaviour. 76 (1): 143–152. doi:10.1016/j.anbehav.2008.01.016. S2CID   53177480.
  14. ↑ Alcock, John (2001). Animal Behavior: An Evolutionary Approach. Sunderland, MA: Sinauer Associates.
  15. ↑ Quinn, J. L.; Cresswell, W. (2006). "Testing domains of danger in the selfish herd: sparrowhawks target widely spaced redshanks in flocks". Proceedings of the Royal Society B: Biological Sciences. 273 (1600): 2521–2526. doi:10.1098/rspb.2006.3612. PMC   1634896 . PMID   16959644.
  16. ↑ Hesse, R. (1937). Ecological Animal Geography. J. Wiley & Sons.
  17. ↑ King, Andrew J.; Wilson, Alan M.; Wilshin, Simon D.; Lowe, John; Haddadi, Hamed; Hailes, Stephen; Morton, A. Jennifer (2012). "Selfish-herd behaviour of sheep under threat" (PDF). Current Biology. 22 (14): R561–R562. doi: 10.1016/j.cub.2012.05.008 . PMID   22835787. S2CID   208514093.
  18. ↑ McClure, Melanie; Emma Despland (2010). "Collective Foraging Patterns of Field Colonies of Malacosoma disstria Caterpillars". The Canadian Entomologist. 142 (5): 473–480. doi:10.4039/n10-001. S2CID   86385536.
  19. ↑ Krakauer, D. (1995). "Groups confuse predators by exploiting perceptual bottlenecks: A connectionist model of the confusion effect". Behavioral Ecology and Sociobiology. 36 (6): 421–429. doi:10.1007/BF00177338. S2CID   22967420.
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