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Polydomy enhances foraging performance in ant colonies.

ABSTRACT: Collective foraging confers benefits in terms of reduced predation risk and access to social information, but it heightens local competition when resources are limited. In social insects, resource limitation has been suggested as a possible cause for the typical decrease in per capita productivity observed with increasing colony size, a phenomenon known as Michener's paradox. Polydomy (distribution of a colony's brood and workers across multiple nests) is believed to help circumvent this paradox through its positive effect on foraging efficiency, but there is still little supporting evidence for this hypothesis. Here, we show experimentally that polydomy enhances the foraging performance of food-deprived Temnothorax nylanderi ant colonies via several mechanisms. First, polydomy influences task allocation within colonies, resulting in faster retrieval of protein resources. Second, communication between sister nests reduces search times for far away resources. Third, colonies move queens, brood and workers across available nest sites in response to spatial heterogeneities in protein and carbohydrate resources. This suggests that polydomy represents a flexible mechanism for space occupancy, helping ant colonies adjust to the environment.

SUBMITTER: Stroeymeyt N

PROVIDER: S-EPMC5413928 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Polydomy enhances foraging performance in ant colonies.

Stroeymeyt N N Joye P P Keller L L

Proceedings. Biological sciences 20170401 1853

Collective foraging confers benefits in terms of reduced predation risk and access to social information, but it heightens local competition when resources are limited. In social insects, resource limitation has been suggested as a possible cause for the typical decrease in <i>per capita</i> productivity observed with increasing colony size, a phenomenon known as Michener's paradox. Polydomy (distribution of a colony's brood and workers across multiple nests) is believed to help circumvent this ...[more]

PMID: 28446699

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Project description:Allocation of goods is a key feature in defining the connection between the individual and the collective scale in any society. Both the process by which goods are to be distributed, and the resulting allocation to the members of the society may affect the success of the population as a whole. One of the most striking natural examples of a highly successful cooperative society is the ant colony which often acts as a single superorganism. In particular, each individual within the ant colony has a "communal stomach" which is used to store and share food with the other colony members by mouth to mouth feeding. Sharing food between communal stomachs allows the colony as a whole to get its food requirements and, more so, allows each individual within the colony to reach its nutritional intake target. The vast majority of colony members do not forage independently but obtain their food through secondary interactions in which food is exchanged between individuals. The global effect of this exchange is not well understood. To gain better understanding into this process we used fluorescence imaging to measure how food from a single external source is distributed and mixed within a Camponotus sanctus ant colony. Using entropic measures to quantify food-blending, we show that while collected food flows into all parts of the colony it mixes only partly. We show that mixing is controlled by the ants' interaction rule which implies that only a fraction of the maximal potential is actually transferred. This rule leads to a robust blending process: i.e., neither the exact food volume that is transferred, nor the interaction schedule are essential to generate the global outcome. Finally, we show how the ants' interaction rules may optimize a trade-off between fast dissemination and efficient mixing. Our results regarding the distribution of a single food source provide a baseline for future studies on distributed regulation of multiple food sources in social insect colonies.

Project description:INTRODUCTION: The accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains. METHODS: We present a side-chain modelling method, pacoPacker, which uses a parallel ant colony optimisation strategy based on sharing a single pheromone matrix. This parallel approach combines different sources of energy functions and generates protein side-chain conformations with the lowest energies jointly determined by the various energy functions. We further optimised the selected rotamers to construct subrotamer by rotamer minimisation, which reasonably improved the discreteness of the rotamer library. RESULTS: We focused on improving the accuracy of side-chain conformation prediction. For a testing set of 442 proteins, 87.19% of X1 and 77.11% of X12 angles were predicted correctly within 40° of the X-ray positions. We compared the accuracy of pacoPacker with state-of-the-art methods, such as CIS-RR and SCWRL4. We analysed the results from different perspectives, in terms of protein chain and individual residues. In this comprehensive benchmark testing, 51.5% of proteins within a length of 400 amino acids predicted by pacoPacker were superior to the results of CIS-RR and SCWRL4 simultaneously. Finally, we also showed the advantage of using the subrotamers strategy. All results confirmed that our parallel approach is competitive to state-of-the-art solutions for packing side chains. CONCLUSIONS: This parallel approach combines various sources of searching intelligence and energy functions to pack protein side chains. It provides a frame-work for combining different inaccuracy/usefulness objective functions by designing parallel heuristic search algorithms.

Dataset Information

Polydomy enhances foraging performance in ant colonies.

Publications

Polydomy enhances foraging performance in ant colonies.

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