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Task allocation and partitioning refers to the way that tasks are chosen, assigned, subdivided, and coordinated (here, within a single colony of social insects). Closely associated are issues of communication that enable these actions to occur. This entry focuses exclusively on social insects. For information on human task allocation and partitioning, see division of labour, task analysis, and workflow.


  • Task allocation "... is the process that results in specific workers being engaged in specific tasks, in numbers appropriate to the current situation. [It] operates without any central or hierarchical control..."[1] The concept of task allocation is individual-centric. It focuses on decisions by individuals about what task to perform. However, different biomathematical models give different weights to inter-individual interactions vs. environmental stimuli.[1]
  • Task partitioning refers to the division of one task into sequential actions done by more than one individual.[2] The focus here is on the task, and its division, rather than on the individuals performing it. For example, "hygienic behavior" is a task in which worker bees uncap and remove diseased brood cells that may be affected by American foulbrood (Paenibacillus larvae) or the parasitic mite Varroa destructor.[3] In this case, individual bees often focus on either uncapping or removing diseased brood. Therefore the task is partitioned, and performed by multiple individuals.[4]


Social living provides a multitude of advantages to its practitioners, including predation risk reduction, environmental buffering, food procurement, and possible mating advantages. The most advanced form of sociality is eusociality, characterized by overlapping generations, cooperative care of the young, and reproductive division of labor, which includes sterility or near-sterility of the overwhelming majority of colony members. With few exceptions, all the practitioners of eusociality are insects of the orders Hymenoptera (ants, bees, and wasps), Isoptera (termites), Thysanoptera (thrips), and Hemiptera (aphids).[5][6] Social insects have been extraordinarily successful ecologically and evolutionarily. This success has at its most pronounced produced colonies 1) having a persistence many times the lifespan of most individuals of the colony, and 2) numbering thousands or even millions of individuals. Social insects can exhibit division of labor with respect to non-reproductive tasks, in addition to the aforementioned reproductive one. In some cases this takes the form of markedly different, alternative morphological development (polymorphism), as in the case of soldier castes in ants, termites, thrips, and aphids, while in other cases it is age-based (temporal polyethism), as with honey bee foragers, who are the oldest members of the colony (with the exception of the queen). Evolutionary biologists are still debating the fitness-advantage gained by social insects due to their advanced division of labor and task allocation, but hypotheses include: increased resilience against a fluctuating environment, reduced energy costs of continuously switching tasks, increased longevity of the colony as a whole, or reduced rate of pathogen transmission.[7][8] Division of labor, large colony sizes, temporally-changing colony needs, and the value of adaptability and efficiency under Darwinian competition, all form a theoretical basis favoring the existence of evolved communication in social insects.[9][10][11] Beyond the rationale, there is well-documented empirical evidence of communication related to tasks; examples include the waggle dance of honey bee foragers, trail marking by ant foragers, and the propagation via pheromones of an alarm state in Africanized honey bees.

Worker Polymorphism

One of the most well known mechanisms of task allocation is worker polymorphism, where workers within a colony have morphological differences. This difference in size is determined by the amount of food workers are fed as larvae, and is set once workers emerge from their pupae. Workers may vary in size, bodily proportions, or both. Body sizes may vary continuously or occur in multiple distinct forms. An excellent example of the former is in bumblebees (Bombus spp.). Unlike most bee species, in which workers are monomorphic, bumblebee workers have a normal distribution of body sizes, from very small to very large. The largest workers may be ten times the mass of the smallest workers. Worker size is correlated with several tasks: larger workers tend to forage, while smaller workers tend to perform brood care and nest thermoregulation. Size also affects task efficiency. Larger workers are better at learning, have better vision, carry more weight, and fly at a greater range of temperatures. However, smaller workers are more resistant to starvation.[12] In many ants and termites, on the other hand, workers vary in both size and bodily proportions, which have a bimodal distribution. This is present in approximately one in six ant genera. In most of these there are two developmentally distinct pathways, or castes, into which workers can develop. Typically members of the smaller caste are called minors and members of the larger caste are called majors or soldiers. There is often variation in size within each caste. The term soldiers may be apt, as in Cephalotes, but in many species members of the larger caste act primarily as foragers or food processors. In a few ant species, such as certain Pheidole species, there is a third caste, called supersoldiers.

Temporal polyethism

Temporal polyethism is a mechanism of task allocation, and is ubiquitous among eusocial insect colonies. Tasks in a colony are allocated among workers based on their age. Newly emerged workers perform tasks within the nest, such as brood care and nest maintenance, and progress to tasks outside the nest, such as foraging, nest defense, and corpse removal as they age. In honeybees, the youngest workers exclusively clean cells, which is then followed by tasks related to brood care and nest maintenance from about 2–11 days of age. From 11– 20 days, they transition to receiving and storing food from foragers, and at about 20 days workers begin to forage.[13] Similar temporal polyethism patterns can be seen in even primitive species of wasps, such as Ropalidia marginata. Young workers feed larvae, and then transition to nest building tasks, followed by foraging.[14] Many species of ants also display this pattern.[15] This pattern is not rigid, though. Workers of certain ages have strong tendencies to perform certain tasks, but may perform other tasks if there is enough need. For instance, removing young workers from the nest will cause foragers, especially younger foragers, to revert to tasks such as caring for brood.[16] These changes in task preference are caused by epigenetic changes over the life of the individual. Honeybee workers of different ages show substantial differences in DNA methylation, which causes differences in gene expression. Reverting foragers to nurses by removing younger workers causes changes in DNA methylation similar to younger workers.[17] Temporal polyethism is not adaptive because of maximized efficiency; indeed older workers are actually more efficient at brood care than younger workers in some ant species.[16] Rather it allows workers with the lowest remaining life expectancy to perform the most dangerous tasks. Older workers tend to perform riskier tasks, such as foraging, which has high risks of predation and parasitism, while younger workers perform less dangerous tasks, such as brood care. If workers experience injuries, which shortens their life expectancies, they will start foraging sooner than healthy workers of the same age.[18]

Response-Threshold Model

A dominant theory of explaining the self-organized division of labor in social insect societies such as honey bee colonies is the Response-Threshold Model. It predicts that individual worker bees have inherent thresholds to stimuli associated with different tasks. Individuals with the lowest thresholds will preferentially perform that task.[7] Stimuli could include the “search time” that elapses while a foraging bee waits to unload her nectar and pollen to a receiver bee at the hive, the smell of diseased brood cells, or any other combination of environmental inputs that an individual worker bee encounters.[19] The Response-Threshold Model only provides for effective task allocation in the honey bee colony if thresholds are varied among individual workers. This variation originates from the considerable genetic diversity among worker daughters of a colony due to the queen’s multiple matings.[20]

Network representation of tasks and communication

Numerous scientists have used a social network approach to model communication in animals, including that related to task performance.[21][22] A network is pictorially represented as a graph, but can equivalently be represented as an adjacency list or adjacency matrix.[23] Traditionally, workers are the nodes of the graph, but Fewell prefers to make the tasks the nodes, with workers as the links.[22][24] O'Donnell has coined the term "worker connectivity" to stand for "communicative interactions that link a colony's workers in a social network and affect task performance".[24] He has pointed out that connectivity provides three adaptive advantages compared to individual direct perception of needs:[24]

  1. It increases both the physical and temporal reach of information. With connectivity, information can travel farther and faster, and additionally can persist longer, including both direct persistence (i.e. through pheromones), memory effects, and by initiating a sequence of events.
  2. It can help overcome task inertia and burnout, and push workers into performing hazardous tasks. For reasons of indirect fitness, this latter stimulus should not be necessary if all workers in the colony are highly related genetically, but that is not always the case.
  3. Key individuals may possess superior knowledge, or have catalytic roles. Examples, respectively, are a sentry who has detected an intruder, or the colony queen.

O'Donnell provides a comprehensive survey, with examples, of factors that have a large bearing on worker connectivity.[24] They include:

  • graph degree
  • size of the interacting group, especially if the network has a modular structure
  • sender distribution (i.e. a small number of controllers vs. numerous senders)
  • strength of the interaction effect, which includes strength of the signal sent, recipient sensitivity, and signal persistence (i.e. pheromone signal vs. sound waves)
  • recipient memory, and its decay function
  • socially-transmitted inhibitory signals, as not all interactions provide positive stimulus
  • specificity of both the signal and recipient response
  • signal and sensory modalities, and activity and interaction rates

Task taxonomy and complexity

Anderson, Franks, and McShea have broken down insect tasks (and subtasks) into a hierarchical taxonomy; their focus is on task partitioning and its complexity implications. They classify tasks as individual, group, team, or partitioned; classification of a task depends on whether there are multiple vs. individual workers, whether there is division of labor, and whether subtasks are done concurrently or sequentially. Note that in their classification, in order for an action to be considered a task, it must contribute positively to inclusive fitness; if it must be combined with other actions to achieve that goal, it is considered to be a subtask. In their simple model, they award 1, 2, or 3 points to the different tasks and subtasks, depending on its above classification. Summing all tasks and subtasks point values down through all levels of nesting allows any task to be given a score that roughly ranks relative complexity of actions.[25] See also the review of task partitioning by Ratnieks and Anderson.[2]

Note: model-building

All models are simplified abstractions of the real-life situation. There exists a basic tradeoff between model precision and parameter precision. A fixed amount of information collected, will, if split amongst the many parameters of an overly precise model, result in at least some of the parameters being represented by inadequate sample sizes.[26] Because of the often limited quantities and limited precision of data from which to calculate parameters values in non-human behavior studies, such models should generally be kept simple. Therefore we generally should not expect models for social insect task allocation or task partitioning to be as elaborate as human workflow ones, for example.

Metrics for division of labor

With increased data, more elaborate metrics for division of labor within the colony become possible. Gorelick and Bertram survey the applicability of metrics taken from a wide range of other fields. They argue that a single output statistic is desirable, to permit comparisons across different population sizes and different numbers of tasks. But they also argue that the input to the function should be a matrix representation (of time spent by each individual on each task), in order to provide the function with better data. They conclude that "... normalized matrix-input generalizations of Shannon's and Simpson's index ... should be the indices of choice when one wants to simultaneously examine division of labor amongst all individuals in a population".[27] Note that these indexes, used as metrics of biodiversity, now find a place measuring division of labor.

See also


  1. 1.0 1.1 Deborah M. Gordon (1996). The organization of work in social insect colonies. Nature 380 (6570): 121–124.
  2. 2.0 2.1 Francis L. W. Ratnieks & Carl Anderson (1999). Task partitioning in insect societies. Insectes Sociaux 47 (2): 95–108.
  3. Arathi, H. S., I. Burns, and M. Spivak. 2000. Ethology of hygienic behaviour in the honey bee Apis mellifera L-(Hymenoptera : Apidae): Behavioural repertoire of hygienic bees. Ethology 106:365-379.
  4. Arathi, H. S., and M. Spivak. 2001. Influence of colony genotypic composition on the performance of hygienic behaviour in the honeybee, Apis mellifera L. Animal Behaviour 62:57-66.
  5. John R. Krebs & Nicholas B. Davies (1987). An Introduction to Behavioural Ecology, 2nd, Blackwell Scientific Publications.
  6. Ross H. Crozier & Pekka Pamilo (1996). "Introduction" Evolution of Social Insect Colonies. Sex Allocation and Kin Selection, 4–8, Oxford University Press.
  7. 7.0 7.1 Duarte, A., I. Pen, L. Keller, and F. J. Weissing. 2012. Evolution of self-organized division of labor in a response threshold model. Behavioral Ecology and Sociobiology 66:947-957.
  8. Wakano, J. Y., K. Nakata, and N. Yamamura. 1998. Dynamic model of optimal age polyethism in social insects under stable and fluctuating environments. Journal of Theoretical Biology 193:153-165.
  9. Carl Anderson & Daniel W. McShea (2001). Individual versus social complexity, with particular reference to ant colonies. Biological Reviews 76 (2): 211–237.
  10. Sasha R. X. Dall, Luc-Alain Giraldeau, Ola Olsson, John M. McNamara & David W. Stephens (2005). Information and its use by animals in evolutionary ecology. Trends in Ecology & Evolution 20 (4): 187–193.
  11. Aaron E. Hirsh & Deborah M. Gordon (2001). Distributed problem solving in social insects. Annals of Mathematics and Artificial Intelligence 31 (1–4): 199–221.
  12. Couvillon, MJ, Jandt JM, Duong NHI, A Dornhaus (2010). Ontogeny of worker body size distribution in bumble bee (Bombus impatiens) colonies. Ecological Entomology 35: 424–435.
  13. Thomas D. Seeley (1982). Adaptive significance of the age polyethism schedule in honeybee colonies. Behavioral Ecology and Sociobiology 11 (4): 287–293.
  14. Dhruba Naug & Raghavendra Gadagkar (1998). The role of age in temporal polyethism in a primitively eusocial wasp. Behavioral Ecology and Sociobiology 42 (1): 37–47.
  15. Bert Hölldobler & E. O. Wilson (1990). The Ants, Cambridge, MA: Harvard University Press.
  16. 16.0 16.1 Muscedere, ML, Willey TA, Traniello JFA (2009). Age and task efficiency in the ant Pheidole dentata: young minor workers are not specialist nurses. Animal Behavior 77: 911–918.
  17. Herb, BR, Wolschin F, Hansen K, Aryee MJ, Langmead B, Irizarry R, Amdam GV, & AP Feinbert (2012). Reversible switching between epigenetic states in honeybee behavioral subcastes. Nature Neuroscience 15: 1371–1375.
  18. Kuszewska, K, Woyciechowski M (2012). Reversion in honeybee, Apis mellifera, workers with different life expectancies. Animal Behaviour 85: 247–253.
  19. Thenius, R., T. Schmickl, and K. Crailsheim. 2008. Optimisation of a honeybee-colony's energetics via social learning based on queuing delays. Connection Science 20:193-210.
  20. Tarapore, D., D. Floreano, and L. Keller. 2010. Task-dependent influence of genetic architecture and mating frequency on division of labour in social insect societies. Behavioral Ecology and Sociobiology 64:675-684.
  21. Deborah M. Gordon (2003). The organization of work in social insect colonies. Complexity 8 (1): 43–46.
  22. 22.0 22.1 Jennifer H. Fewell (2003). Social insect networks. Science 301 (5461): 1867–1870.
  23. Michael Goodrich & Roberto Tamassia (2002). Algorithm Design.
  24. 24.0 24.1 24.2 24.3 S. O'Donnell & S. J. Bulova (2007). Worker connectivity: a review of the design of worker communication systems and their effects on task performance in insect societies. Insectes Sociaux 54 (3): 203–210.
  25. Carl Anderson, Nigel R. Franks & Daniel W. McShea (2001). The complexity and hierarchical structure of tasks in insect societies. Animal Behaviour 62 (4): 643–651.
  26. Stephen P. Ellner & John Guckenheimer (2006). "Building dynamic models" Dynamic Models in Biology, 289–290, Princeton University Press.
  27. R. Gorelick & S. M. Bertram (2007). Quantifying division of labor: borrowing tools from sociology, sociobiology, information theory, landscape ecology, and biogeography. Insectes Sociaux 54 (2): 105–112.

Further reading

  • Guy Theraulaz, Eric Bonabeau, Ricard V. Solé, Bertrand Schatz & Jean-Louis Deneubourg (2002). Task partitioning in a ponerine ant. Journal of Theoretical Biology 215: 481–489.
  • Chris Tofts (1993). Algorithms for task allocation in ants (a study of temporal polyethism theory). Bulletin of Mathematical Biology 55 (5): 891–918.

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