Behavioural Processes 102 (2014) 51–61

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Behavioural Processes journal homepage: www.elsevier.com/locate/behavproc

Beginnings of a synthetic approach to desert ant navigation Ken Cheng a,∗ , Patrick Schultheiss a , Sebastian Schwarz b , Antoine Wystrach c , Rüdiger Wehner d a

Department of Biological Sciences, Macquarie University, Australia Department of Psychology, Neuroscience & Behaviour, McMaster University, Canada School of Life Sciences, University of Sussex, United Kingdom d Brain Research Institute, University of Zürich, Switzerland b c

a r t i c l e

i n f o

Article history: Received 1 July 2013 Received in revised form 27 September 2013 Accepted 5 October 2013 Keywords: Desert ant Navigation Path integration Visual panorama Systematic search Synthetic approach

a b s t r a c t In a synthetic approach to studying navigational abilities in desert ants, we review recent work comparing ants living in different visual ecologies. Those living in a visually rich habitat strewn with tussocks, bushes, and trees are compared to those living in visually barren salt pans, as exemplified by the Central Australian Melophorus bagoti and the North African Cataglyphis fortis, respectively. In bare habitats the navigator must rely primarily on path integration, keeping track of the distance and direction in which it has travelled, while in visually rich habitats the navigator can rely more on guidance by the visual panorama. Consistent with these expectations, C. fortis performs better than M. bagoti on various measures of precision at path integration. In contrast, M. bagoti learned a visually based associative task better than C. fortis, the latter generally failing at the task. Both these ants, however, exhibit a similar pattern of systematic search as a ‘back up’ strategy when other navigational strategies fail. A newly investigated salt-pan species of Melophorus (as yet unnamed) resembles C. fortis more, and its congener M. bagoti less, in its path integration. The synthetic approach would benefit from comparing more species chosen to address evolutionary questions. This article is part of a Special Issue entitled: CO3 2013. © 2013 Elsevier B.V. All rights reserved.

Contents 1. 2. 3. 4. 5. 6. 7.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thermophilic scavengers and their navigational toolkits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Precision in path integration in C. fortis and M. bagoti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. fortis stays with path integration longer than does M. bagoti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. bagoti is better than C. fortis in a task of visual associative learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Systematic searching: similarities and differences between species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction On a January day in 2001, two of the authors (KC, RW) and Sibylle Wehner pulled in at the parking lot at Simpson’s Gap, a gorgeous desert place in the West MacDonnell ranges ∼18 km west of Alice Springs, Australia. As the group stepped out of the car,

∗ Corresponding author at: Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia. Tel.: +61 2 98508613; fax: +61 2 98509231. E-mail address: [email protected] (K. Cheng). 0376-6357/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.beproc.2013.10.001

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a red ant with long legs dashed quickly across the hot bitumen. Eureka! Thus began the study of navigation, foraging, and learning in the thermophilic red honey ant Melophorus bagoti (Cheng et al., 2009). More than a decade later, the study of M. bagoti now complements research on the far more studied desert ants of North Africa, genus Cataglyphis (see Wehner, 2013, for a personal historical account), as well as desert ants in southern Africa, genus Ocymyrmex (Wehner, 2003). We have learned something about its foraging ecology (Muser et al., 2005; Schultheiss and Nooten, 2013), its use of the surrounding panorama (Graham and Cheng, 2009; Wystrach et al., 2011a, 2012), its route following

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(Kohler and Wehner, 2005; Narendra, 2007b; Sommer et al., 2008; Wystrach et al., 2011b), and compared its performance with other desert ants (Bühlmann et al., 2011; Schwarz and Cheng, 2010). The study of Melophorus ants has now expanded to a species (as yet unnamed) that lives on the salt pans of South Australia (Schultheiss et al., 2012). Comparing ants in different habitats that fill a similar ecological niche of a thermophilic scavenger has yielded interesting similarities and differences that form the topic of the present account. Research on navigation in desert ants on different continents reflects to a large extent Kamil’s (1987) synthetic approach to the study of animal intelligence, especially in the comparisons between species. The synthetic approach focuses on intelligent behaviour that an animal needs to use in its natural life, much more so than on laboratory tasks that experimenters can impose in standardized conditions. Lab work is not eschewed, but often inspired and informed by field work in the natural habitats of the animals. A wider range of intelligent behaviour was called for in Kamil’s (1987) paper, including numerical, timing, and spatial abilities, as well as placing the animal’s behaviour in an appropriate ecological and evolutionary context. In Kamil’s own work on food-storing corvids for example, the interest started with field observations suggesting excellent spatial memory in the food-storing Clark’s nutcracker (Nucifraga columbiana; Tomback, 1980), which stores and retrieves tens of thousands of food caches over a winter (Vander Wall and Balda, 1977). Lab work inspired by such findings confirmed the role of spatial memory (Kamil and Balda, 1985). Further comparative work with a number of corvids showed that the Clark’s nutcracker, the most prolific food-storer among North American corvids, performs best on lab tasks of spatial memory, but did not stand out in a lab task requiring memory for colour (Olson et al., 1995). Birds that are more prolific food storers also remember caches stored in the lab for longer (Bednekoff et al., 1997). More recent work has expanded to compare corvids on other kinds of tasks, such as transitive inference (Bond et al., 2010). The work on desert ant navigation focuses on an important task in their natural life, that of finding their way to food sources solitarily – for desert ants are solitary foragers – and then finding their way back home again after suitable prey has been found. Most of the research takes place in the ants’ natural habitats, in the form of field experiments, but lab work complements the enterprise when it is needed, for example in examining the eyes (Schwarz et al., 2011b) or the brains of ants (Stieb et al., 2010). The subjects of study travel the scale of distance in the range of their normal travels on foraging excursions, unlike, for example, the much smaller and restricted arenas typically foisted on lab rodents in the study of rodent navigation. Functional and mechanistic questions have both been entertained in the course of the research. And with the most recent comparisons across genera, questions of evolution are beginning to be addressed about the navigational toolkit of these animals. In our account, we will show that all desert ants possess a basic navigational toolkit. Natural selection from living in different habitats, however, has drawn different emphases and specializations. We discuss briefly the bases for such specializations. First, however, we need to present the key players in the story.

2. Thermophilic scavengers and their navigational toolkits The desert ants of the genera Cataglyphis (in North Africa), Ocymyrmex (in southern Africa), and Melophorus (in Australia) all fill the niche of thermophilic scavengers (Wehner, 1987; Wehner and Wehner, 2011). These ants specialize in heat tolerance, with one of our study species, M. bagoti, being the most heat tolerant ant on its continent (Christian and Morton, 1992). This allows the

ants to forage in the heat of the day in summer, when it is too hot for other foragers. M. bagoti, for example, shares a geographic niche with other ants (of the genera Iridomyrmex, Camponotus, and others), but when the ground temperature exceeds about 50 ◦ C, only they are out foraging (Schultheiss and Nooten, 2013). They forage solitarily, running on long legs (Sommer and Wehner, 2012) and scavenging dead animal bits, largely arthropods (Wehner et al., 1983). In ants that live in plant-rich habitats, plant materials are also collected (Muser et al., 2005; Schultheiss and Nooten, 2013; Schultheiss et al., 2012). One species, Cataglyphis floricola in southwestern Spain, even uses the latter in the form of petals as its main food source (Cerdá et al., 1992). Their navigational toolkit includes a number of what Wiener et al. (2011) called spatial primitives, such as the encoding of compass direction, based on both celestial and terrestrial cues, and distance travelled or odometric measure. It is possible that the more complex level of spatial constructs is also encoded. The major systems in the toolkit are generally recognized as path integration, use of the visual panorama, and systematic searching (Wystrach et al., 2013b), although a fourth system of backtracking has just been proposed for M. bagoti (Wystrach et al., 2013b). Less well investigated are the use of cues of other modalities, including vibrational, magnetic (Buehlmann et al., 2012) and olfactory ones (Buehlmann et al., 2012; Steck et al., 2009; Wolf and Wehner, 2000), and a different compass mediated by the ocelli (Schwarz et al., 2011a, 2011c) rather than the dorsal rim area of the compound eyes (Wehner, 1994). In path integration, the navigator keeps track of the straightline distance and direction that it has travelled from a starting point (typically home), and uses the calculated vector for homing (Wehner and Srinivasan, 2003). This system depends on registering the direction in which the ant is travelling, the distance travelled en route (odometry), and importantly, integrating the two kinds of information, so that the traveller encodes which direction a particular step has moved in. The directional component is based largely, but not solely, on a celestial compass derived from the pattern of polarized light in the sky. Light is scattered in a systematic fashion as it enters the Earth’s atmosphere (Wehner, 1994). A visual system with appropriate receptors and processing systems can perceive a pattern in the polarized light that indicates the direction of the sun. The position of the sun itself is also used as a cue (Wehner and Müller, 2006), as are spectral patterns (Wehner, 1994). The latter refer to the distribution of different wavelengths of light reflected by terrestrial objects as a function of the position of the sun. The pattern of polarized light, spanning the entire sky, is not surprisingly weighted more than the position of the sun (Wehner and Müller, 2006). Odometry is dependent largely on a stride integrator that functions like a step counter (Wittlinger et al., 2006, 2007), although the pattern of optic flow beneath the ant is also used to some small extent (Ronacher and Wehner, 1995). These studies were all on the North African C. fortis. In using the visual panorama, some visual cues in the surrounding panorama are used to determine a direction of travel. These cues might encompass large segments or the entire visual surround (Graham and Cheng, 2009; Wystrach et al., 2011a, 2012). Thus, the skyline is used by M. bagoti as a terrestrial compass source (Graham and Cheng, 2009). The skyline is a record of the heights of terrestrial objects in the visual panorama. Graham and Cheng (2009) showed that desert ants would follow the dictates of an artificially constructed skyline made of black cloth. The contour of the cloth enclosure matched roughly the skyline heights measured at the feeder to which the ants were trained to visit, with the elevations matched at 15◦ intervals. Even large landmarks, obvious and prominent to human observers, might not be used by the ants when the rest of the visual panorama is unfamiliar (Wystrach et al., 2011a). This and other results led to proposals that the entire panoramic

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terrestrial scene guides ants as some kind of terrestrial visual compass (Graham et al., 2010; Wystrach et al., 2012, 2013b). Different models of how this is done have been proposed (e.g., Baddeley et al., 2012; Möller, 2012; see Möller, 2012 for a brief overview), and this topic forms an agenda for present research. M. bagoti, at least, backtracks under appropriate conditions (Wystrach et al., 2013b). The ants reverse the last direction in which they were travelling, when (1) their vector based on path integration is near zero, (2) the current scene looks unfamiliar, and (3) when they have just seen a familiar scene, that surrounding their nest. According to Wystrach et al. (2013b), backtracking functions to move the ant in the direction that is most likely to lead to some familiar visual scene after sudden displacement, for instance by wind. Thus, if a forager nearing her nest is blown off by wind, and ends up somewhere unfamiliar, it is probable that she has gone beyond the nest along the route that she had travelled on. Had she been blown back along her route of travel, the scene should look familiar. Even with these strategies operating normally, the homing ant might miss the nest because of inevitable errors of biological systems. We have often observed returning ants searching in the vicinity of their nests for the entrance on a normal excursion. In such cases, the ant resorts to a back up strategy of systematic searching. In all systematic searches, the ant loops around the starting point of the search in ever larger loops, often returning to the starting point of search (Wehner and Srinivasan, 1981). But as we

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explain later, searching in a familiar area, such as in the vicinity of a nest or an oft-visited food site, appears to be a different process from searching in unfamiliar territory (Schultheiss and Cheng, 2011, 2013; Schultheiss et al., 2013). While we focus on comparing desert ants, other ants – and for that matter other insects – share the major tools of the toolkit. Thus, rainforest ants Gigantiops destructor integrate a path (Beugnon et al., 2005) and follow signature routes (Macquart et al., 2006), wood ants, Formica japonica, rely mainly on distant panoramic cues, which can fully outcompete information provided by the path integrator (Fukushi, 2001; Fukushi and Wehner, 2004), and nightactive bull ants Myrmecia pyriformis use a celestial compass and the visual panorama for directional cues (Reid et al., 2011). Even traillaying ants learn to use the panorama (review: Collett and Zeil, 1998). And the navigation of honeybees, as well as other kinds of learning, has been much studied (reviews: Avarguès-Weber et al., 2011; Perry et al., 2013). Different species of desert ants live in different habitats, and in the comparisons we are about to present, we address how these differences in habitats might have shaped their use of the navigational toolkit. Some species live in bare salt pans (Fig. 1A and C), in which natural terrestrial visual cues are largely absent. These include the much studied North African ant C. fortis (Fig. 1A, taken from Wehner, 2012), and a ‘new’ (taxonomically speaking) Melophorus ant that has yet to be named (which at present we will call Melophorus sp.; see Schultheiss et al., 2012 and Fig. 1C). Other ants, such

Fig. 1. Different habitats where desert ants live, with photos of the ants. (A) Chott el Djerid, a typical North African salt pan habitat for C. fortis. Reprinted from Wehner (2012,). (B) Near Alice Springs Airport, Alice Springs, Australia, a typical habitat for M. bagoti. C. Eucolo Creek at Island Lagoon, South Australia, a typical habitat for the salt-pan dwelling Melophorus sp. Photo credits: R. Wehner (A), Eliza J. T. Middleton (B, habitat), S. Schwarz (B, ant, and C, habitat and ant).

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SD of group / M of group

0.4 6 m outbound distance 12 m outbound distance

0.3

0.2

0.1

0

Fig. 2. A field near Alice Springs Airport that has been largely cleared of vegetation. Bühlmann et al. (2011) compared a nest of Melophorus bagoti found there with Cataglyphis fortis ants. Photo by Eliza J. T. Middleton.

C. fortis

M. bagoti

Fig. 3. Precision in odometry of Cataglyphis fortis and Melophorus bagoti ants. Ants travelled in a narrow channel (10 cm × 10 cm) for 6 m or 12 m to a feeder. They then returned in a different channel with a bit of food, and the first point at which an ant turned back and initiated back-and-forth searching was noted. The dependent measure is the standard deviation of the group divided by the mean of the group. A number of similar conditions were pooled. Data on C. fortis came from Cheng et al. (2006). Data on M. bagoti from Narendra et al. (2007a).

as M. bagoti, live in visually cluttered habitats (Fig. 1B). In bare habitats, path integration is at a premium because useful terrestrial visual cues are largely absent. We might expect ants in such habitats to specialize in path integration. In a cluttered habitat on the other hand, terrestrial visual cues might be used to form stereotypical routes (e.g., Kohler and Wehner, 2005; Mangan and Webb, 2012; Wystrach et al., 2011b). We may expect ants in such habitats to rely more on terrestrial visual cues for navigation, and to be better able to learn visually based tasks. In what follows, most of the comparisons revolve around C. fortis (a North African salt-pan ant) and M. bagoti (an Australian ant in a cluttered habitat), because these species have been studied the most. 3. Precision in path integration in C. fortis and M. bagoti Most of the comparisons of path integration have been between C. fortis and M. bagoti. The data to be described come largely from experimental set ups in which a feeder at a constant location provisions food for the foragers. After foragers have made regular visits to the feeder for a while, picking up some food to take home on each visit, a forager is tested after she grabs a bit of food in her mandibles at the feeder. Such a forager with booty is motivated to head home. Because C. fortis lives in a bare habitat, our predictions were that it would be more precise in path integration than M. bagoti. Because M. bagoti lives in a visually cluttered landscape and can rely on terrestrial visual cues for guidance for most of its travel, finding a suitable nest for comparing with C. fortis was a challenge. An artificially cleared field near the Alice Springs airport (Fig. 2) was the most ‘level playing field’ we could find. This cleared plot with quite a uniform ring of trees around it was used for launching hot-air balloons, and on it one nest of M. bagoti could be located. We could thus test the precision of path integration of this nest as they travel over terrain resembling what faces the typical C. fortis forager (Bühlmann et al., 2011). C. fortis and balloon-field M. bagoti ants, each in their habitat, were trained to visit a feeder at a constant location, and then displaced to a distant location with some food in their mandibles. The (circular) scatter in their initial heading direction provided a measure of directional precision. Without nearby guiding visual cues, and little by way of distant panorama, the ants had to rely mostly on their sky compass (Wehner, 1994; Wehner and Müller, 2006) to strike an initial heading. On this measure, however, no significant differences between species was evident. This was the case

whether artificial landmarks (black cylinders) were provided along the nest-feeder route or not. These results do not support our predictions. In retrospect, M. bagoti might well need precision in using their sky compass for more than path integration. Wystrach et al. (2012), in modelling their data, suggested that panoramic visual patterns would benefit from being aligned with respect to the sky compass in some situations. The sky compass, serving as a key component of the path integration system, might also function in learning walks of ants. Before they begin foraging, desert ants make short walks in the area of their nest, striking out in different directions and returning to the nest in loops (C. bicolor: Wehner et al., 2004; Ocymyrmex robustior: Müller and Wehner, 2010). O. robustior ‘learners’ turn to face their invisible nest at regular intervals during learning walks, and path integration is said to “provide a scaffold” for learning the view towards the nest (Müller and Wehner, 2010). The sky compass might also help ants recapitulating a familiar route to orient in the correct direction along a route. Thus, the sky compass might serve both path integration and panorama-based navigation in M. bagoti, and precision in the sky compass might be just as important for M. bagoti as for C. fortis. Another aspect of path integration is odometry, estimating the distance travelled on the outbound trip. On this front, we were not limited to the balloon-field M. bagoti nest because narrow channels could be constructed that largely excluded any views outside of the channel. Ants of each species travelled a fixed distance to a feeder in channels of the same construction. Ants with food were then displaced to a long channel, and their initial run in the homeward direction before they turned back and started a back-and-forth search pattern constituted an odometric estimate (C. fortis: Cheng et al., 2006; Sommer and Wehner, 2004; M. bagoti: Narendra et al., 2007a). On average, both species travelled roughly the outbound distance in channels before starting to search, but the scatter across animals was significantly larger in M. bagoti, thus showing less odometric precision (Fig. 3). The species difference was confirmed statistically by a (conservative) test of differences in variance between multiple groups (O’Brien’s test), and was found at both 6 m and 12 m outbound distance. The results confirm that C. fortis is better at this aspect of path integration. Behind this species difference in odometric performance is one commonality: both species perform as well as they do from trial 1, and no improvement with practice is found. Both the studies on

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C. fortis (Cheng et al., 2006) and M. bagoti (Narendra et al., 2007a) trained different groups of ants with different numbers of trials (visits to the feeder) before testing, and no significant differences were found between these groups in either species. Repeated training does not improve path integration on the natural open field either (Merkle and Wehner, 2009b). This makes some functional sense, as the ant needs to be good enough to return to the nest on its first foraging trip or it will perish. Perhaps most telling are comparisons of performance in path integration on the open field, comparing the balloon-field M. bagoti nest with C. fortis foragers. In one condition, Bühlmann et al. (2011) let ants at a feeder run home with food on the training field itself, with no displacement of the animals. The situation matched the ants’ training runs between nest and feeder completely. The authors observed that M. bagoti ants were far worse at finding their nest. This is in good part because the scatter in their odometric estimates (SD) was 4.7 times larger than the scatter in C. fortis. Strikingly, most C. fortis ants (18/20) found their way directly to their nest without exhibiting looping search patterns at all, whereas only 2/18 M. bagoti ants managed to find their nest without having to search in loops for it. These differences likewise confirm that C. fortis is better at path integration than M. bagoti. Intriguing and telling in the comparisons of odometric precision are recent data from Melophorus sp. (work in prep.), the Melophorus ant that lives on barren salt pans (Fig. 1C). Measured at 4 m outbound distance over open field (i.e., not in channels), the variation in odometric estimates of Melophorus sp., adjusted by the outbound distance, resembled that found in C. fortis more than that found in M. bagoti. Melophorus sp. lives in a habitat with few if any usable terrestrial visual cues for navigation and must rely primarily on path integration. A species signature is evident, but the line is drawn between ecological habitats and not between genera.

4. C. fortis stays with path integration longer than does M. bagoti In a bare habitat, path integration and systematic search are at a premium because the navigator lacks usable visual cues in the panorama. One might expect such a navigator to stick with the vector estimated from path integration for its entirety before starting to search systematically. When a route is rich in visual cues on the other hand, and the navigator has been displaced off its familiar route, the option of searching for familiar route cues might compete with the strategy of path integration. Such a navigator might be expected to turn to systematic searching before the calculated vector has been run off. These expectations have been confirmed in comparing cluttered-habitat ants (M. bagoti) with salt-pan ants (both C. fortis and the salt-pan Melophorus sp.). Considering first comparisons between C. fortis and M. bagoti, Bühlmann et al. (2011) trained ants to visit a feeder regularly, and then took trained ants one at a time from the feeder to a distant test site. They measured the length of the initial homing run on the open field on the test, and found that under a variety of conditions, M. bagoti from the nest on the balloon-field ran off a shorter distance than did C. fortis before initiating systematic search, some 10–20% shorter. This was the case when landmarks were provided en route in both training and test situations, when no additional landmarks were provided, or when landmarks en route were provided in training but absent on test. In addition, when a nest-defining landmark is presented at an earlier stage of the ant’s homebound trip, i.e., before the fictive position of the nest is reached, M. bagoti gets attracted by this landmark much more readily than does C. fortis (Bregy et al., 2008; Bühlmann et al., 2011). Interestingly, initial data on the saltpan Melophorus sp. show that they behave more like C. fortis, which inhabit the same kind of bare habitat, than like their congener M.

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bagoti (Fig. 4, from Schultheiss et al., 2012). When trained and tested on the open field, Melophorus sp. ran the full outbound distance before initiating search. A species signature is again evident, with the line again drawn between ecological habitats and not genera. In addition to a species signature is what we might call a signature of environmental mismatch. In C. fortis, if artificial landmarks along the route were removed on a test, they run slightly but significantly less than the full outbound distance (Bühlmann et al., 2011). In M. bagoti, the proportion of the outbound distance that they run before initiating search varies across conditions of testing (Bühlmann et al., 2011; Cheng et al., 2012; Narendra, 2007a, 2007b). As already mentioned, if trained and tested in narrow channels that blocked the visual surround, M. bagoti run the full outbound distance back (Narendra, 2007a; Narendra et al., 2007a, 2008). When displaced from one open-field location to another open-field location, they run some ∼75–90% of the outbound distance on average (Bühlmann et al., 2011; Cheng et al., 2012). And when displaced from one of their typical cluttered habitats to an unfamiliar location, they run ∼45–50% of the outbound distance on average (Cheng et al., 2012; Narendra, 2007b). Species differences are manifested not only in the initiation point of systematic search, but also before that, while the ants are running off their vector or a part thereof. We have observed that when M. bagoti ants are running home on unfamiliar territory, or even if they are a small distance away from their usual route, their paths meander, compared with their largely straight runs on a familiar route. In recent studies, the straightness of the path that they run has been quantified (Cheng et al., 2012; Schwarz and Wystrach, 2011; Wystrach et al., 2011b). A ‘dose response’ curve has been found in that the more the mismatch between the scene at the release point and the scene on a familiar route, the more the ants meander, and the less straight their paths. In Cheng et al. (2012), ants from 3 nests in typical cluttered habitats were displaced from a feeder to the open balloon-launching field. Their initial paths were less straight than those of the nest living on the balloon-launching field. Comparing M. bagoti with C. fortis ants under similar conditions (that is, using the M. bagoti nest on the balloon-launching field), Bühlmann et al. (2011) found that the paths of C. fortis were straighter. This was the case whether experimental landmarks were provided along the route or not in training and testing. We interpret the meandering as a sign of conflict with the strategy of systematic searching, with systematic search eventually winning out when the ant turns and makes looping searches. In this regard too, both a species signature and a signature of environmental mismatch are evident. In sum, under similar conditions, C. fortis runs a longer proportion of their calculated vector than M. bagoti does before engaging in systematic search. And the runs of C. fortis are straighter. Both differences indicate a higher propensity to rely on path integration in C. fortis. The one test performed so far on the salt-pan Melophorus sp. shows that they, like C. fortis, run off the full vector before starting systematic search.

5. M. bagoti is better than C. fortis in a task of visual associative learning An ant living in a visually cluttered habitat is expected to rely more on terrestrial visual cues. It might be expected to do better on a visually based learning task. One study compared M. bagoti and C. fortis explicitly in the same visual learning task (Schwarz and Cheng, 2010). The ants were trained and tested in channels. After coming out to a feeder, they were trained to choose one side of a bifurcation along the way home, in a version of a Y-maze. One side was coloured black, while the other side was white, and the correct choice was the black side. The task was mastered within

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Fig. 4. Comparison of the average homing distance between different desert ant species after displacement from a familiar feeder to an unfamiliar test field; the position of the fictive nest is marked by a small cross. Melophorus sp. and Cataglyphis fortis inhabit similar visual habitats and exhibit similar homing paths. On average, they run off their entire vector before they start to search for the fictive nest. M. bagoti ants differ in their homing behaviour depending on whether they are trained in visually open or cluttered surroundings. The ants were tested on an open field. Only when living and being trained in open surroundings do homing paths of M. bagoti resemble those of the other two species, although the ants still run off a shorter proportion of their path-integration vector. In contrast, when taken from their typical cluttered habitats M. bagoti ants search on average much earlier for their fictive nest. Paths of C. fortis (outbound distance of 10 m) are adapted with permission from the Journal of Experimental Biology (Bühlmann et al., 2011), paths of M. bagoti (outbound distance of 12 m) are from Cheng et al. (2012), and paths of Melophorus sp. (outbound distance of 3 m) are from Schultheiss et al. (2012). For purposes of clarity, most groups show paths that were selected randomly from a larger number of paths (Melophorus sp.: n = 10 of 36, C. fortis: n = 23 of 23, ‘open’ M. bagoti: n = 8 of 20, ‘cluttered’ M. bagoti: n = 12 of 25). Reprinted with permission from Schultheiss et al. (2012) (http://www.publish.csiro.au/nid/90/paper/ZO12096.htm, Fig. 9).

30 trials by a slim majority of M. bagoti ants, all of which continued successfully on subsequent tests. In sharp contrast, only 1 of 60 C. fortis foragers succeeded within 30 trials of training and maintained the success on the subsequent tests. The study included a rare C. fortis nest located in a visually cluttered habitat, as well as a nest provided with artificial landmarks, neither of which had any successful ants. This one data set supported our comparative predictions. On the other hand, all desert ants can learn to use a set of experimental landmarks to help locate their nest (C. fortis: Ziegler and Wehner, 1997; C. bicolor: Wehner et al., 1996; Wehner and Räber, 1979; M. bagoti: Narendra et al., 2007b). These studies used 2 to 4 black cylinders, in the middle of which was located the nest. Ants were displaced to a distant test field with the landmarks set up for testing. The results from these studies, however, are not comparable. Ziegler and Wehner (1997) reported training C. fortis ants for at least 2 days, and thus the exact amount of training was not specified. Narendra et al. (2007b) trained M. bagoti ants for up to 15 trials, spread unsystematically over days depending on how frequently the ants returned to the feeder. Learning was best after 15 training trials spread over more than 2 days. Test performance was also not comparable: M. bagoti ants, having been displaced to a distant test site, faced a different panoramic surround from that found at their nest. The significance of panoramic visual information was not fully appreciated at that time. Although species differences in learning to use a set of experimentally provided visual cues (landmarks) cannot be ascertained at this point, underlying similarities between all desert ant species

in this tool can be pointed out once the goal location with respect to these cues has been learned. Thus, in both C. fortis (Ziegler and Wehner, 1997) and M. bagoti (Narendra et al., 2007b), these visually based memories last a long time, as long a delay as experimenters cared to impose, probably meaning that the memories last the lifetime of the ant. Doubling the distance of each landmark from the fictive target (the place where the nest would be had the ants not been displaced on a test) leads to a breakdown in searching in C. bicolor (Wehner et al., 1996; Wehner and Räber, 1979) and in M. bagoti (Narendra et al., 2007b). If the landmarks double their distance and also double their size, however, both these species then search at the centre of the array. Some matching of the projected size of these objects, beyond matching their mean azimuths, seems to matter to these ants. Important theoretical progress has been made on this front (Baddeley et al., 2012; Möller, 2012; Wystrach et al., 2013a), but much theoretical and empirical research remain to be done to specify what panoramic characteristics are used in matching. Another similarity across species is the propensity to follow a stereotypical route when visual cues along the route are available. Melophorus bagoti’s major mode of travel is following a route that is similar from one trip to the next, as demonstrated in a number of studies now (Kohler and Wehner, 2005; Sommer et al., 2008; Wehner et al., 2006; Wystrach et al., 2011b). Ants from a C. fortis nest found in an unusually cluttered habitat also recapitulate stereotypical routes (Wehner et al., 1996). When displaced near their nest back to a part of their route, these ants readily run off their route once more. Ants of other Cataglyphis species living in

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cluttered habitats also recapitulate stereotypical routes (C. velox: Mangan and Webb, 2012). 6. Systematic searching: similarities and differences between species Sometimes a forager may fail to find a specific location it has visited before, for example the nest entrance or a food location. In this case it engages a ‘backup’ mechanism, the systematic search behaviour. As mentioned, the searching ant moves around in ever expanding loops, while re-visiting the vicinity where it expects to find the target repeatedly. This basic pattern of expanding loops is found in every species of desert ant studied so far (C. fortis, C. bicolor, M. bagoti, Melophorus sp., Fig. 5). The overall spread of these search paths (the area that is covered) is flexible and depends on the context of the search and the experience of the forager, and can be used as a proxy for search accuracy. In ants searching for their nest, those having run off a longer vector before being displaced for a test engage in a larger spread in the subsequent nest search, probably an adaptation to an increase in the navigational error of path integration (C. fortis: Merkle et al., 2006; M. bagoti: Schultheiss and Cheng, 2011). Further work on M. bagoti has revealed a similar pattern in relation to navigation in a visually familiar environment. When these ants search for their nest in the vicinity of their actual nest, their precision is greater (their search spread is smaller) in a visually rich environment with experimentally added landmarks around their nest than in a normal ‘unenriched’ visual environment (Schultheiss et al., 2013). In outbound M. bagoti foragers searching for a known food source, the size of the search can also depend on the type of food that the ant is looking for. Searches for protein food are larger than searches for carbohydrate food, which matches the different distributions of naturally occurring food items of these kinds (Schultheiss

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and Cheng, 2013). The size of food searches in C. fortis is additionally influenced by productivity and familiarity of food sites (Bolek et al., 2012). It remains to be seen whether the other species of desert ants respond to variations in food type, quantity and familiarity in a similar fashion. In other forcimine ants (Formica pallidefulva, formerly F. schaufussi), however, similar differences in patterns of searching for carbohydrates vs. protein have been found (Fourcassié and Traniello, 1994; Traniello et al., 1992). Another line of inquiry has focussed on the movement strategies of searching desert ants, and what they can tell us about the mechanistic basis of searching behaviour. For this, an animal’s trajectory is discretised into a series of straight-line movements (segments), connected by instances of re-orientation, or turns. In M. bagoti, the distribution of search segment lengths displays an exponential distribution of movement lengths with lots of short segments and very few long segments when searching in a familiar visual context, either for a single food source (Schultheiss and Cheng, 2013) or for the nest entrance (Schultheiss et al., 2013). This pattern changes to a ‘two-process’ strategy, a mixture of two exponentials, when the ants are displaced into a distant test-field and search for their nest in an unfamiliar visual environment (Schultheiss and Cheng, 2011). The picture is different in the salt-pan dwelling Melophorus sp., which displays a single-exponential strategy (similar to a Brownian walk) when searching for the nest in a distant test-field (work in prep.). However, the visual appearance of the distant test-field in this scenario (the open, featureless salt-pan) will look almost identical to the familiar nest environment, even though the ant has never visited this place before. Based purely on visual input, the searching Melophorus sp. foragers may not be able to discriminate between the two. In the Namibian desert ant Ocymyrmex robustior even a single nearby landmark can concentrate search movements and reduce search time dramatically (Wehner and Müller, 2010). So far, these data seem to indicate that visual scene familiarity may

Fig. 5. Expanding loops in the search pattern of desert ants: (A) Cataglyphis bicolor, (B) Melophorus bagoti. Each panel shows one example path from a single searching ant. The distance of a searching ant from the origin is plotted against path length, showing that the path has a looping structure (the ant repeatedly returns close to the origin), and that loop size gradually increases. Panel A is adapted from Wehner and Srinivasan (1981) (Fig. 8) with kind permission from Springer Science+Business Media, panel B shows data from Schultheiss and Cheng (2011), but not presented in that work.

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play an important role in shaping the search strategies of desert ants, and the notion of familiarity plays a key explanatory role in a recent account (Wystrach et al., 2013b), to which we will return in considering differences across species. Yet, the distribution of segment lengths in the search strategies of Cataglyphis ants remains to be studied. Intriguingly, the exponential search strategies of desert ants are distinct from the strategies of another well-studied hymenopteran, honeybees Apis mellifera. In similar contexts, i.e., for nest or food searches in unfamiliar environments, the search paths of bees have scale-free power-law properties, consistent with the execution of so-called Lévy walks (Reynolds et al., 2007a, 2007b). In Lévy walks, the distribution of segment lengths (l) obeys a power function, distributed as l− , with the exponent ␮ around 2 to be optimal. Compared to exponential strategies, these have a much “heavier” tail, with a higher frequency of very long movement lengths. These Lévy searches are optimal for finding single targets in unfamiliar environments (Reynolds, 2008), so why are they seen in bees but not in ants? One possibility is that a property of bees’ odometric system is conducive to producing Lévy search patterns. In bees, odometric errors are proportional to the distance being estimated (Cheng et al., 1999). Theoretically, Lévy walks could be derived from this property (Reynolds et al., 2013). In desert ants, odometric errors are not proportional (Sommer and Wehner, 2004), and Lévy walks cannot be derived. It is therefore possible that the double-exponential strategy of M. bagoti is a way of approximating a scale-free Lévy walk in unfamiliar environments, which would be the optimal strategy (Reynolds et al., 2014).

7. Discussion While the study of desert ant navigation has had a history going back more than a century, at least to Santschi’s (1911, 1913) work (for a biographical account of this almost forgotten scientist and his work, see Wehner, 1990), the systematic comparison of desert ants living in different habitats has had a far shorter history. What we have gathered together here supports predictions that ants living in different habitats would specialize in different aspects of navigation. All desert ants, however, seem to share a basic navigational toolkit of spatial primitives (Wiener et al., 2011) consisting at least of path integration, visually guided navigation based on panoramic terrestrial cues, and systematic search. It is how they weight these different tools that seems to differ according to ecological habitat. Based on some recent models of insect navigation, we can speculate on some of what the adaptive specializations consist in. Far more comparisons are necessary, however, for a synthetic approach to desert ant navigation, or to expand further to ants in general or even to hymenopterans or insects as a group. In fact, many aspects of navigation are shared by all terrestrial navigators, including the sun, visual panorama, and the laws of geometry governing the relations between paths of travel and points on the surface of the Earth. This suggests the likelihood of convergent mechanisms, and even deep homologies (for example, with vertebrate animals) are not out of the question. We elaborate on these points in this discussion. Based on the navigational cues available in different habitats, we predicted that ants living in visually bare habitats would specialize more in path integration, while ants living in visually rich habitats would specialize more in navigation based on terrestrial visual cues. A number of differences reviewed here confirm these expectations. Cataglyphis fortis, living in a bare salt-pan habitat, performs better in various ways in path integration than does M. bagoti, living in a visually cluttered habitat. Compared with M. bagoti, C. fortis is more precise in odometry, estimating the distance travelled on a trip (Cheng et al., 2006; Narendra et al., 2007a), and

better at finding its nest by using path integration, without having to resort to searching (Bühlmann et al., 2011). Cataglyphis fortis also runs off a longer portion of the estimated vector before resorting to searching, in comparison with M. bagoti under various conditions (Bühlmann et al., 2011). Importantly, M. bagoti’s congener Melophorus sp., which lives in a bare salt-pan habitat like C. fortis, looks more like C. fortis than like M. bagoti in its path integration (Schultheiss et al., 2012; work in prep.). In the use of visual cues, explicit comparisons are limited to date, and far more needs to be done. In one task of visual associative learning (choosing a black side in a Y-maze), M. bagoti performed much better than C. fortis (Schwarz and Cheng, 2010). These differences between desert ants living in different habitats should be seen against the backdrop of a common navigational toolkit likely extending well beyond desert ants to other ants, other hymenopterans, and probably most insects and many arthropods. If ecological habitats have driven the evolution of navigational strategies, it is in specialization in relying more on one tool or the other, and probably not in the fabrication of a brand new ‘tool’. Thus, path integration, navigation based on the visual panorama, and systematic searching are found in all these desert ants. While M. bagoti may not be as precise as C. fortis or Melophorus sp., it has been clearly demonstrated to integrate a path travelled (Narendra, 2007a, 2007b; Wehner et al., 2006). And while C. fortis may not perform as well as M. bagoti in a visual associative task, many studies have shown that it uses experimentally provided landmarks to locate a target (e.g., Ziegler and Wehner, 1997). And C. fortis, C. bicolor, C. albicans, M. bagoti, and Melophorus sp. all resort to systematic search strategies when other navigational strategies fail (Merkle et al., 2006; Merkle and Wehner, 2009a; Schultheiss and Cheng, 2011, 2013; Schultheiss et al., 2013; Wehner and Srinivasan, 1981; work in prep.). Recent models of insect navigation (Collett, 2012; Cruse and Wehner, 2011; Wystrach et al., 2013b) suggest how the adaptive specialization of relying more or less on a particular navigational system might be realized neurobiologically, and this in turn suggests how natural selection or ontogenetic experience might find a locus for their effects. The basic idea in these models is that the various systems or modules are not switched on one at a time, as if a switch can only connect with one system at a time. Rather, they are all in operation simultaneously, converging on a central summator that sums the inputs in a weighted basis. The operation of one system or module means that it has a high weight while the other modules have near zero weights. Different conditions might dictate which system is assigned more weight. Thus, Wystrach et al. (2013b) posit explicitly that systematic search has a continuous low weight. That makes this module a fall-back option, running when current conditions make the weights for all the other modules very low. The path integration system is weighted by the magnitude of the scalar component (vector length), while the influence of visual matching of terrestrial cues is weighted by the familiarity of the current view. A signature of the weighted average scheme, whatever its form or underlying specifications, is that sometimes, more than one system can be witnessed in operation. Empirically, this means that the animal follows a dictate that is an average of the dictates of different systems. Reid et al. (2011) set the pattern of polarized light and terrestrial panorama in conflict by using a polarizing filter with which they can sysetmatically manipulate the polarized light. They found that night active bull ants Myrmecia pyriformis averaged the dictates of polarized light and terrestrial panorama in such conflicts. Collett (2012) displaced C. fortis ants on their familiar outbound journey to a feeder, another way of setting the dictates of path integration in conflict with those of the terrestrial visual panorama. He found that the ants struck a course intermediate between the dictates of those two systems. We have found similar intermediate courses in

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homebound M. bagoti ants in work in preparation. And we have also interpreted the meandering during path integration or view based matching as a partial tendency for systematic searching (see Section 4). A scheme of weighted averaging points to a locus, the weights, on which both natural selection and ontogenetic experience may act, at least in shaping the propensity to rely more on one system rather than another. For example to break off path integration earlier or later and switch to searching, the threshold for the point at which searching dominates over following path integration may be changed by adjusting weights appropriately. Species differences might consist in assigning more weight, or potentiating the inputs of one system to the summator. Likewise, experience in navigating under different circumstances might adjust those weights. Whether such a scheme might also contribute to differences in performance at odometry or visual associative learning, for example by affecting attentional or motivational factors, remains to be determined. It would be interesting to investigate whether such a weighting scheme might apply to other taxa, such as the propensity for some food-storing birds to rely on spatial cues for remembering a location (Brodbeck, 1994; Brodbeck and Shettleworth, 1995). Schemes of weighted averaging have certainly been proposed for vertebrate animals in navigation (Cheng et al., 2006; Collett et al., 1986). It would also be exciting to unravel the neurobiological and genomic bases of such a plausible computational scheme. Systematic search is considered a ‘fall back’ strategy when other navigational strategies fail. As such, some common characteristics across species may be expected. For example, one ideal strategy that has been discussed (Reynolds et al., 2013; Wehner and Srinivasan, 1981) is an expanding spiral pattern. If the spiral is perfectly executed, and perception of the target when one comes near it is fail-safe, this is the most efficient search strategy possible. Of course, the antecedent conditions following the “if” are not realized in any error-prone biological system, and no animal has shown a spiral systematic search pattern (but see the “hidden spiral” in Müller and Wehner, 1994). The expanding looping searches cater to error-prone searching found in animals, and can be found in other taxa than ants (e.g., isopods: Hoffmann, 1983a, 1983b, 1985a, 1985b). On the other hand, the target of search may be different in different species and search conditions, perhaps generating differences in systematic search strategies. When looking for a nest or a food site in a visually bare habitat, the searcher needs to locate the exact spot of the target. But when looking for a target in an unfamiliar but visually cluttered habitat, finding some familiar scenery is usually good enough for finding the target (Wystrach et al., 2012). Once in familiar territory, reliable route strategies can usually deliver. Further comparisons of systematic searching can be fruitful in a synthetic approach to desert ant navigation. Indeed, the comparisons that we have presented need to be expanded. Mostly, we have single comparisons between a pair of species, in a sense amounting to n = 1. In the near future, parallel comparisons between Cataglyphis species on the one hand, and between Melophorus species on the other hand, would expand the comparative base, and also provide some ‘phylogenetic control’, in that pairs (or more) of congeners may be compared. Recently, not only has work begun on a salt-pan Melophorus species, but also on a Cataglyphis species, C. velox, living in a visually cluttered Spanish habitat resembling M. bagoti’s mix of tussocks, bushes, and trees (Mangan and Webb, 2012; see their Fig. 1). Like M. bagoti (Kohler and Wehner, 2005; Sommer et al., 2008), C. velox relies a lot on stereotypical routes (Mangan and Webb, 2012). Cataglyphis velox spontaneously forms multiple routes. Whether it is more like M. bagoti in other regards than like C. fortis remains to be investigated. Cataglyphis bicolor, in former years much studied before its habitat was encroached upon by economic development (Wehner, 2013), is also often found in visually cluttered habitats.

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Looking further afield, a far larger crop of species needs to be examined to unravel some evolutionary questions about navigation in desert ants. Large comparative studies requiring the cooperation of many teams, as trumpeted in a recent manifesto by MacLean and numerous co-authors (2012), are likely necessary to trace the origins of basic navigational abilities such as path integration or systematic search strategies. These abilities, along with other basic forms of learning, may be widespread among arthropods, or perhaps inherited from a common ancestor. The scientific world is biased against reporting negative data, but in tracing the origins of learning and cognitive abilities, it is absolutely necessary to document both negative and rudimentary cases of the ability (Perry et al., 2013). Any terrestrial animal navigator (or even a mechanical robot) faces a set of frequently encountered problems. Given a particular outbound path, the direct homebound path is fully determined by the laws of geometry as applied to a limited region of the Earth’s surface. The visual panorama changes in predictable ways as one travels: objects that one approaches take up more of the view while objects that one moves away from take up less of the view. The sun moves in the same way across the day for all. And so on. This suggests the likelihood of convergent mechanisms or at least similarities across the navigational toolkits of diverse taxa, for example between insects and vertebrate animals (Cheng, 2012; Wiener et al., 2011; Wystrach and Graham, 2012). Recently, a deep homology between parts of arthropod and vertebrate brains (central complex in arthropods and basal ganglia in vertebrates) has even been suggested (Strausfeld and Hirth, 2013). Deep homologies in navigational mechanisms are not out of the question. A fully comparative approach (Shettleworth, 2010) should be an integral part of the synthetic approach to animal navigation. Acknowledgements We would like to thank Sibylle Wehner for all her support in launching the research on both Melophorus bagoti and the saltpan Melophorus sp., as well as her help on Cataglyphis research over the years. Funding for the desert ant research reported here has come from the Australian Research Council (DP0770300 and DP110100608 to KC and RW), the Swiss National Science Foundation (3675/1-1 to RW), and Macquarie University in supporting PS, SS, and AW. References Avarguès-Weber, A., Deisig, N., Giurfa, M., 2011. Visual cognition in social insects. Ann. Rev. Entomol. 56, 423–443. Baddeley, B., Graham, P., Husbands, P., Philippides, A., 2012. A model of ant route navigation driven by scene familiarity. PLoS Comp. Biol. 8, e1002336. Bednekoff, P.A., Balda, R.P., Kamil, A.C., Hile, A.G., 1997. Long-term spatial memory in four seed-caching corvid species. Anim. Behav. 53, 335–341. Beugnon, G., Lachaud, J.-P., Chagné, P., 2005. Use of long-term stored vector information in the neotropical ant Gigantiops destructor. J. Insect Behav. 18, 415–432. Bolek, S., Wittlinger, M., Wolf, H., 2012. Establishing food site vectors in desert ants. J. Exp. Biol. 215, 653–656. Bond, A.B., Wei, C.A., Kamil, A.C., 2010. Cognitive representation in transitive inference: a comparison of four corvid species. Behav. Proc. 85, 283–292. Bregy, P., Sommer, S., Wehner, R., 2008. Nest-mark orientation versus vector navigation in desert ants. J. Exp. Biol. 211, 1868–1873. Brodbeck, D.R., 1994. Memory for spatial and local cues: a comparison of a storing and a nonstoring species. Anim. Learn. Behav. 22, 119–133. Brodbeck, D.R., Shettleworth, S.J., 1995. Matching location and color of a compound stimulus: comparison of a food-storing and a nonstoring bird species. J. Exp. Psychol.: Anim. Behav. Process. 21, 64–77. Buehlmann, C., Hansson, B.S., Knaden, M., 2012. Desert ants learn vibration and magnetic landmarks. PLoS One 7, e33117. Bühlmann, C., Cheng, K., Wehner, R., 2011. Vector-based and landmark-guided navigation in desert ants inhabiting landmark-free and landmark-rich environments. J. Exp. Biol. 214, 2845–2853. Cerdá, X., Retana, J., Carpintero, S., Cros, S., 1992. Petals as the main resource collected by the ant Cataglyphis floricola (Hymenoptera Formicidae). Sociobiology 20, 315–319.

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