Why do house-hunting ants recruit in both directions?
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- Planqué, R., Dechaume-Moncharmont, FX., Franks, N.R. et al. Naturwissenschaften (2007) 94: 911. doi:10.1007/s00114-007-0273-8
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To perform tasks, organisms often use multiple procedures. Explaining the breadth of such behavioural repertoires is not always straightforward. During house hunting, colonies of Temnothorax albipennis ants use a range of behaviours to organise their emigrations. In particular, the ants use tandem running to recruit naïve ants to potential nest sites. Initially, they use forward tandem runs (FTRs) in which one leader takes a single follower along the route from the old nest to the new one. Later, they use reverse tandem runs (RTRs) in the opposite direction. Tandem runs are used to teach active ants the route between the nests, so that they can be involved quickly in nest evaluation and subsequent recruitment. When a quorum of decision-makers at the new nest is reached, they switch to carrying nestmates. This is three times faster than tandem running. As a rule, having more FTRs early should thus mean faster emigrations, thereby reducing the colony’s vulnerability. So why do ants use RTRs, which are both slow and late? It would seem quicker and simpler for the ants to use more FTRs (and higher quorums) to have enough knowledgeable ants to do all the carrying. In this study, we present the first testable theoretical explanation for the role of RTRs. We set out to find the theoretically fastest emigration strategy for a set of emigration conditions. We conclude that RTRs can have a positive effect on emigration speed if FTRs are limited. In these cases, low quorums together with lots of reverse tandem running give the fastest emigration.
KeywordsRecruitment methods Social insects Tandem running Temnothorax albipennis Collective behaviour
Organisms often employ more than one mechanism to accomplish a task. For instance, animals typically navigate with multiple ‘input channels’. The classic example is homing by the rock dove Columba livia, for which magnetic fields, the sun, landmarks and geophysical processes have all been shown to be used (Wiltschko and Wiltschko 2003).
The range of behaviours or mechanisms organisms employ may be puzzling. At times, an apparent simplicity is observed. Defence strategies against predators are a well-studied example here. Acacia trees (Acacia spp.) either have chemical defences to ward off herbivores or have symbiotic relationships with protective ants (Rehr et al. 1973). Hosts parasitised by Eurasian cuckoos Cuculus canorus famously reject cuckoo eggs, but never reject cuckoo chicks (Davies 2000). This simplicity may be the result of evolutionary lag but, more interestingly, may also be caused by one strategy making another maladaptive by reducing predator abundance (Planqué et al. 2002; Britton et al. 2007).
Conversely, the portfolio of behaviours may be varied and complex. Different mechanisms may complement one another, and true redundancy is often hard to show (Able and Bingman 1987). Indeed, the existence of a suite of mechanisms against a broad ensemble of predators is readily understandable. One exemplar is the vertebrate immune system (Perelson 2002).
Another striking example of a system in which different mechanisms augment and complement one another, but now at a collective level, is house hunting in social insects. This has become one of the model systems to study distributed decision making in animals. When the nest is destroyed, the colony has to decide collectively where to settle next during a time of crisis (Franks et al. 2003a). Individual ants or bees have been shown to combine sophisticated assessments of potential nest sites (Seeley 1977; Seeley and Morse 1978; Mallon and Franks 2000; Franks et al. 2003b) with various recruitment mechanisms to collate information, and thus make collective decisions (Mallon et al. 2001; Pratt et al. 2002; Pratt 2005; Seeley and Visscher 2003, 2004; Visscher 2007).
A typical emigration by a colony of Temnothorax albipennis may be described as follows. When the old nest is destroyed, a fraction of ants goes out scouting to find a new home. Upon finding a nest, the nest is assessed (Mallon and Franks 2000) and ants start recruiting other ants to it with a latency that is inversely proportional to the perceived nest quality (Mallon et al. 2001), using a process called forward tandem running (Möglich et al. 1974). During a forward tandem run, a knowledgeable ant teams up with a naïve ant. The leader slowly progresses towards the new nest, each time waiting for the follower to catch up, thereby teaching her the way (Franks and Richardson 2006). Through this slow recruitment process, information on the location of the new nest spreads, and recruiter numbers increase. Once a nest population reaches a certain quorum threshold, the recruiters switch from slow tandem running to much faster social carrying, and transport the remaining passive ants and brood to the new nests (Pratt et al. 2002; Pratt 2005).
This description has been the basis of several models (Pratt et al. 2002; Pratt et al. 2005; Marshall et al. 2006; Planqué et al. 2006). However, a behaviour commonly employed by these ants is usually not included (but see Pratt et al. 2005) and has never been analysed. After the quorum, recruiter ants are not only engaged in social carrying, but also regularly perform tandem runs from the new back to the old nest. These so-called reverse tandem runs (Möglich 1978) are often more common than forward tandem runs (Mallon et al. 2001; Pratt et al. 2002), but their function is much less well understood.
To maximise fitness, the colony should emigrate as quickly as possible to avoid predation and other hazards. Therefore, during house hunting, a fast build up of recruiters is essential. Why then do ants mix fast carrying with slow reverse tandem running, when they already have forward tandem running at their disposal?
In this paper, the role of reverse tandem running is theoretically investigated. In particular, through the use of mathematical models, we explore under what circumstances reverse tandem running can have a positive influence on emigration speed.
Materials and methods
We present two mathematical models to investigate the possible role of reverse tandem runs in ant colony emigrations. Reverse tandem running does not contribute to the decision-making process of which new nest to choose (Pratt et al. 2002; Franks et al. 2003a). We, therefore, restrict ourselves to emigrations to one new nest only.
Both tandem running and social carrying involve a pair of ants from two different classes. Hence, recruitment can only occur if ants of both participating classes are available;
Once the quorum has been met, recruiters cannot carry and perform reverse tandem runs simultaneously (we also assume recruiters are not involved in other activities than these two).
The interaction between different classes of ants has been modelled using simple interaction terms. We assume that ants of both classes are well mixed in the part of the arena (or nest) where they meet. With populations of ants of size X and Y meeting, the number of ants that on average meet is then proportional to XY/(X + Y). Importantly, the smallest class limits the interaction rate, as is to be expected.
We also explored a number of other models in which some assumptions were relaxed. These are briefly discussed in the final section of this paper.
Values or ranges, where applicable, for the parameters used in models 1 and 2 depicted in Fig. 1
Fraction of active ants
Fraction of post-quorum reverse tandem running time
Rate at which active ants at old nest become scouts (ant − 1 min − 1)
Rate at which ants following tandem runs become recruiters (ant − 1 min − 1)
Rate at which passive ants are carried to new nest (ant − 1 min − 1)
Rate at which scouts independently become recruiters (ant − 1 min − 1)
Note that, although models 1 and 2 broadly give similar predictions, they differ in the amount of post-quorum time spent on reverse tandem runs. In model 1, this reaches a full 100% in model 1, but never so in model 2.
Overall, the models predict that reverse tandem running should be used more than forward tandem running, and the quorum threshold lowered, if the recruitment latency increases by decreasing k (scouting ants wait longer before starting their first recruitment act), in combination with either a decreasing fraction of active ants F, or an increasing scouting parameter μ. For all but very large F, the optimal quorum threshold corresponded to the time when all active ants have left the old nest to go scouting. In the absence of multiple new nests, the decision when to switch from forward tandem running to social carrying is thus best made at the old nest. Recruiters should thus apply the following rule: Continue forward tandem running until there are no ants left to perform them with and then switch to social carrying; if few forward tandem runs have been performed (by the recruiters), combine carrying with reverse tandem runs; otherwise, do not.
To maximise their fitness, ants should try to achieve the fastest emigrations to minimise vulnerability (Franks et al. 2003a; Franks et al. 2003b). Therefore, the active ants either have to become scouts, discover a new site and then become recruiters or wait at the old nest until a recruiter leads them to the new nest. Both of these processes may be hampered: When all the active ants go out scouting, recruiter numbers slowly increase if the new nest is hard to find or if those few cannot find any active ant back at the old nest to tandem run with. In terms of the models, this could occur if scouts slowly become recruiters (low value for k), in combination with either a small class of active ants at the old nest (F is small) or all active ants having gone scouting (high value for scouting rate μ). Under either or both of these circumstances, the model predicts that ants should not waste time trying to recruit by forward tandem runs but should do the next best thing and use a low quorum threshold to quickly switch to carrying. The recruiters should then invest a fraction of their time to recruit scouts or carried ants using reverse tandem runs, thus boosting the recruiter population and speeding up the emigration.
Conversely, the model also predicts that reverse tandem runs should not be used if either the new nest is easy to find (recruiter numbers then build quickly anyway), or when there are many ants to follow a forward tandem run.
These predictions fit quite well with previous experimental work. Ants have been shown to leave their intact old nest if the new nest is sufficiently better, but have lower standards when their nest is destroyed (Dornhaus et al. 2004). In these experiments, reverse tandem runs were mainly observed when the old nest was destroyed, combined with few forward tandem runs. The model offers a simple explanation for this: The greater panic might have caused fewer scouts to remain at the old nest, thereby obstructing early recruitment.
Whilst investigating speed–accuracy trade-offs, Franks et al. (2003a) found lower quorums under harsh than under mild conditions. This again fits with the models. On the other hand, the models also predict higher numbers of reverse tandem runs. In the experiments, this difference in reverse tandem running activity between mild and harsher conditions was found to be non-significant (Franks et al. 2003a).
Critique on model 2
Reverse tandem activity
Figure 2 (top left) shows that recruiters in model 1 should use a sequential strategy if F is small (and μ is large): When the quorum is met, they first spend all of their time on reverse tandem runs until all scouts have become recruiters, and then switch to carrying. In contrast, when F is large, recruiters mix tandem running and carrying. This qualitative difference may be understood as follows.
The total number of recruiters is bounded by FN, the number of active ants in the colony. As F decreases, the remaining recruiters take longer to carry all the passive ants. Hence, the time costs for not having the recruiters increases and the time to recruit the remaining scouts decreases (as there are fewer scouts too). Hence, in this situation, recruiters should devote their post-quorum time, first, all on reverse tandem running before starting carrying.
When F is large, the reverse argument applies. With less passive ants, there should be less emphasis on additional recruitment by reverse tandem running. One does not have to make many hands if the work was light to start with.
Note that this behaviour for F →0 is different for model 2. In this paper, we never observe sequential strategies (Fig. 2, top right), as there is no end to building recruiter numbers but by completing the entire emigration.
This argument also explains another difference between these models: The number of reverse tandem runs during an emigration. In model 1, there is a clear maximum for intermediate F, whereas in model 2 the number of reverse tandems strictly decreases with F (Fig. 3, second from left, top and bottom).
Nonlinearities in the models and divisions between active and passive ants
Contrary to the models in this paper, two previous models of house hunting by T. albipennis ants (Pratt et al. 2002; Planqué et al. 2006) assumed linear terms for tandem running and social carrying. There, these processes occurred at rates only proportional to the number of recruiters. The predictions of the current models proved to be strongly dependent on the assumption of non-linearity of these terms. The corresponding linear models predicted that reverse tandem running should not be used for practically any parameter choices in F and μ or k.
Another ingredient in this model shared with the previous (Pratt et al. 2002; Planqué et al. 2006), models of this collective decision-making system is the division between active and passive ants. There is as yet little experimental evidence suggesting this division really exists. All we know at present is that a limited fraction of ants is actively engaged during an emigration. We have thus also explored models in which this division was absent, using both linear and non-linear interaction terms such as those in models 1 and 2 presented in this paper. In none of these models did reverse tandem running contribute to the optimal emigration speed. The division between active and passive ants is thus a crucial ingredient for reverse tandem running to have a positive impact on emigration speed, which should be experimentally validated.
Several hypotheses on the potential role of reverse tandem running have been put forward (Pratt et al. 2002). First, the ants might have a “home nest”, which changes during the emigration, thereby reversing the direction of any recruitment events from “home” to another nest (Pratt et al. 2002). If true, this would predict a change of direction when about half of the colony had been displaced. This is not in agreement with the available data (Mallon et al. 2001). Moreover, this hypothesis does not offer a suggestion why tandem running often occurs early in the emigration. In other words, it might explain the direction, but not the occurrence itself.
Second, it has been suggested that reverse tandem running may re-allocate recruitment (Pratt et al. 2002). Again, this does not fit the available data from Mallon et al. (2001). Reverse tandem runs were nearly always observed between the best nest and the old nest. The models in this paper do not incorporate choice between nest sites, but we conjecture that early flexible commitment (Planqué et al. 2006) will be more efficient in redirecting ants to better nests than late recruitment. Other experimental results also corroborate that reverse tandem running does not influence the decision-making process (Franks et al. 2003a).
The first models in which reverse tandem runs have been explicitly incorporated to analyse their role have yielded clear predictions: under a range of conditions, we expect a negative correlation between levels of early and late recruitment. This finding lends itself well to simple experiments, and we aim to present those in the near future.
The build up of recruiter numbers serves two purposes: to decide on a nest and to increase the number of ants actively involved in transport. The decision-making process and the implementation of this decision are thus conflated. This in itself is a side-effect of the distributed nature of this system. Reverse tandem running may thus be a logical extension to overcome this inherent problem. This suggests that such additional backup behaviours could be a common feature of decentralised collective decision-making systems.
We would like to thank all the members of the Bristol Ant Lab and two anonymous referees for their insightful comments and ideas. RP, NRF, JARM and TK are pleased to acknowledge the support of an EPSRC grant (GR/S78674/01). RP was also kindly supported by the NDNS+ cluster, financed by the National Science Foundation NWO. NRF and FXDM are grateful for the support of a BBSRC grant (E19832).