Task allocation and partitioning of social insects
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
communicationthat 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..." [Gordon D. 1996. The organization of work in social insect colonies. "Nature" 380:121-124. (p.121)] 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. [Gordon D. 1996. The organization of work in social insect colonies. "Nature" 380:121-124. (p.122)]
*task partitioning refers to the division of one task into sequential actions done by more than one individual. [Ratnieks F. and Anderson C. 1999. Task partitioning in insect societies. "Insectes Sociaux". 46:95-108.] The focus here is on the task, and its division, rather than on the individuals performing it.
Social living provides a multitude of advantages to its practitioners, including predation risk reduction, environmental buffering, food procurement, and possible mating advantages. The most extreme 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). [Krebs J. and Davies N. 1987. An Introduction to Behavioural Ecology. 2nd edition. Blackwell Scientific Publications. 389p. (p.291)] [Crozier R. and Pamilo P. 1996. Evolution of Social Insect Colonies. Oxford University Press. 306p. (p.4-8)] 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 (age polyethism), as with honey beeforagers, who are the oldest members of the colony (with the exception of the queen).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. [Anderson C. and McShea D. 2001. Individual versus social complexity, with particular reference to ant colonies. "Biological Reviews". 76:211-237] [Dall S., Giraldeau L-A., Olsson O, McNamara J., and Stephens D. 2005. Information and its use by animals in evolutionary ecology. "TRENDS in Ecology and Evolution" 20:187-193.] [Hirsh A., and Gordon D. 2001. Distributed problem solving in social insects. "Annals of Mathematics and Artificial Intelligence" 31:199-221.] Beyond the rationale, there is well-documented empirical evidence of communication related to tasks; examples include the waggle danceof honey bee foragers, trail marking by ant foragers, and the propagation via pheromones of an alarm state in “Africanized” honey bees.
Network representation of tasks and communication
Numerous scientists have used a
social networkapproach to model communication in animals, including that related to task performance. [Gordon D. 2003. The Organization of Work in Social Insect Colonies. "Complexity". 8:43-46.] [ Fewell J. 2003. Social insect networks. "Science". 301: 1867-1870.] A network is pictorially represented as a graph, but can equivalently be represented as an adjacency listor adjacency matrix. [Goodrich M. and Tamassia R. 2002. Algorithm Design. Wiley. 708 p. (p.296)] Traditionally, workers are the nodes of the graph, but Fewell prefers to make the tasks the nodes, with workers as the links. [O’Donnell S. and Bulova S.J. 2007. Worker connectivity: a review of the design of worker communication systems and their effects on task performance in insect societies. "Insectes Sociaux". 54: 203 – 210. (p.204)] [Fewell J. 2003. Social insect networks. "Science". 301: 1867-1870.] 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”. [O’Donnell S. and Bulova S.J. 2007. Worker connectivity: a review of the design of worker communication systems and their effects on task performance in insect societies. "Insectes Sociaux". 54: 203 – 210.] He [O’Donnell S. and Bulova S.J. 2007. Worker connectivity: a review of the design of worker communication systems and their effects on task performance in insect societies. "Insectes Sociaux". 54: 203 – 210. (p.204)] has pointed out that connectivity provides three adaptive advantages compared to individual direct perception of needs:
# 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.
# 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.
# 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 [ O’Donnell S. and Bulova S.J. 2007. Worker connectivity: a review of the design of worker communication systems and their effects on task performance in insect societies. "Insectes Sociaux". 54: 203 – 210. ] provides a comprehensive survey, with examples, of factors that have a large bearing on worker connectivity. 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. [Anderson C., Franks N., and McShea D. 2001. The complexity and hierarchical structure of tasks in insect societies. "Animal Behaviour". 62: 643-651.] See also the fine review of task partitioning by Ratnieks and Anderson. [Ratnieks F. and Anderson C. 1999. Task partitioning in insect societies. "Insectes Sociaux" 46:95-108]
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. [ Ellner S. and Guckenheimer J. 2006. Dynamic Models in Biology. Princeton University Press. 329p. (p.289-290)] 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
workflowones, 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”. [Gorelick R. and Bertram S.M. 2007. Quantifying division of labor: borrowing tools from sociology, sociobiology, information theory, landscape ecology, and biogeography. "Insectes Sociaux". 54: 105 – 112.] Note that these indexes, used as metrics of
biodiversity, now find a place measuring division of labor.
Patterns of self-organization in ants
* Theraulaz G, Bonabeau E, Sole R, Schatz B, and Deneubourg J-L. 2002. Task partitioning in a Ponerine ant. "Journal of Theoretical Biology". 215:481-489.
* Tofts C. 1993. Algorithms for task allocation in ants. (a study of temporal polyethism: theory). "Bulletin of Mathematical Biology". 55:891-918.
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