Sebastian Schwarz
email: sebastian.schwarz@univ-tlse3.fr

Michael Mangan

Barbara Webb

Antoine Wystrach

Paul Sabatier

Route-following ants respond to alterations of the view sequence

Keywords: Cataglyphis, desert ants, homing, navigation, route-following, view sequence

Ants can navigate by comparing the currently perceived view with memorised views along a familiar foraging route. Models regarding route-following suggest the views are stored and recalled independently of the sequence in which they occur. Hence, the ant only needs to evaluate the instantaneous familiarity of the current view to obtain a heading direction. This study investigates whether ant homing behaviour is influenced by alterations in the sequence of views experienced along a familiar route, using the frequency of stop-and-scan behaviour as an indicator of the ant's navigational uncertainty. Ants were trained to forage between their nest and a feeder which they exited through a short channel before proceeding along the homeward route. In tests, ants were collected before entering the nest and released again in the channel, which was placed either in its original location or halfway along the route. Ants exiting the familiar channel in the middle of the route would thus experience familiar views in a novel sequence. Results show that ants exiting the channel scan significantly more when they find themselves in the middle of the route, compared to when emerging at the expected location near the feeder. This behaviour suggests that previously encountered views influence the recognition of current views, even when these views are highly familiar, revealing a sequence component to route memory. How information about view sequences could be implemented in the insect brain as well as potential alternative explanations to our results are discussed.

INTRODUCTION

Solitary foraging desert ants are expert navigators that seek and retrieve food morsels under extreme heat conditions. When foragers locate a bountiful food source they will shuttle rapidly back and forth along idiosyncratic visually-guided routes [START_REF] Kohler | Idiosyncratic route-based memories in desert ants, Melophorus bagoti: how do they interact with path-integration vectors?[END_REF][START_REF] Mangan | Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox)[END_REF][START_REF] Wystrach | Views, landmarks, and routes: how do desert ants negotiate an obstacle course?[END_REF]. That is, each ant will follow a fixed path to the feeder before returning home by a similarly fixed but different path. These paths are unique to each ant despite their journeys sharing the same start and end points, demonstrating a lack of pheromone guidance. Instead visual information provided by the ants' surroundings is sufficient for route following and individuals can even recover their normal route direction following a displacement either by an experimenter [START_REF] Kohler | Idiosyncratic route-based memories in desert ants, Melophorus bagoti: how do they interact with path-integration vectors?[END_REF][START_REF] Mangan | Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox)[END_REF][START_REF] Sommer | Multiroute memories in desert ants[END_REF] or a wind gust (Wystrach and Schwarz, 2013), suggesting that visual memories (here termed 'views') can be accessed independently of the animal's recent experience. This memory feature is embedded in recently developed computational models of visual route following. A key insight was that if retinotopy is maintained in the view encoding [START_REF] Baddeley | A Model of Ant Route Navigation Driven by Scene Familiarity[END_REF][START_REF] Baddeley | Holistic visual encoding of ant-like routes: Navigation without waypoints[END_REF][START_REF] Collett | Insect navigation: what backward walking reveals about the control of movement[END_REF][START_REF] Möller | A model of ant navigation based on visual prediction[END_REF]Wystrach et al., 2013;[START_REF] Zeil | Catchment areas of panoramic snapshots in outdoor scenes[END_REF], the correct direction to move at any point along a route can be recovered by finding the viewing direction that produces the best match, or least novelty, when compared to the complete set of views stored in a previous traversal of the route. By simply moving along the direction with the least novelty the animal would repeatedly align with the direction it previously travelled and retrace its path. Ardin and colleagues (Ardin et al., 2016a) demonstrated that the circuitry of the insect mushroom body (MB) is ideally suited to measure the novelty of the current 'view' against those previously experienced.

Each view is assumed to create a unique sparse activation pattern in the MB Kenyon cells and can be stored as 'familiar' by reducing the output weights of those cells. Views from novel locations or from familiar locations when facing the wrong direction will produce novel activation patterns and thus still activate the network's output, to trigger steering corrections. Computational models using such novelty-driven MB networks have produced realistic route following behaviours in simulated environments (ants: (Ardin et al., 2016a); bees: [START_REF] Müller | A neural network model for familiarity and context learningduring honeybee foraging flights[END_REF]) and on a mobile robot in a real ant habitat [START_REF] Kodzhabashev | Route following without scanning[END_REF]. Both experimental- [START_REF] Freas | The view from the trees: nocturnal bull ants Myrmecia midas use the surrounding panorama while descending from trees[END_REF][START_REF] Narendra | Mapping the navigational knowledge of individually foraging ants, Myrmecia croslandi[END_REF][START_REF] Wehner | Visual spatial memory in desert ants, Cataglyphis bicolor (Hymenoptera: Formicidae)[END_REF]Wystrach et al., 2011a) and neurobiological data (Ardin et al., 2016b;[START_REF] Webb | Neural mechanisms of insect navigation[END_REF] in ants accommodate the mentioned models although some other processes may also be at work [START_REF] Cartwright | Landmark learning in bees: experiments and models[END_REF][START_REF] Mangan | Modelling place memory in crickets[END_REF][START_REF] Möller | A model of ant navigation based on visual prediction[END_REF][START_REF] Wystrach | Ants might use different viewmatching strategies on and off the route[END_REF].

As noted, a key feature of these models is that 'memory of a route' does not include any information about the sequence in which views are encountered. The agent can tell whether a given view is familiar or not, but cannot tell whether it corresponds to the beginning, the end or any other location along the route. Consequently, the agent has no information about whether two views should be experienced in succession nor any expectation that any particular view will occur after another. In theory, one could present all the views from a familiar route in a random order with no difference in the agents' behaviour.

This simple scene-action control hypothesis is parsimonious , however, data from behavioural studies suggest that the picture may not be so simple [START_REF] Wehner | Visual navigation in insects: coupling of egocentric and geocentric information[END_REF]. Specifically, (Wystrach et al., 2013) showed that ants displaced from their nest to an unfamiliar location do not immediately engage in a systematic search but instead backtrack along their just travelled route bearing. This effect is only present in ants that have been captured at the nest indicative of ants possessing some memory of recent visual experiences. Furthermore, [START_REF] Collett | A desert ant's memory of recent visual experience and the control of route guidance[END_REF]Wystrach et al. 2019) demonstrated that ants forced to retrace their homeward routes twice in succession (moved from the feeder back to the start of their inward route) display a period of confusion where they do not seem to recognise their familiar path. [START_REF] Graham | Insect navigation: do ants live in the now?[END_REF] postulate a series of possible explanations for such behaviours including the use of temporal information about their routes such as the sequence in which views were experienced. This study aims to address this possibility directly by assessing whether ants have knowledge about the sequence of views encountered along their familiar foraging route. It is already known that ants and bees can be trained to learn sequences of patterns [START_REF] Schwarz | Visual discrimination, sequential learning and memory retrieval in the Australian desert ant Melophorus bagoti[END_REF] and motor actions [START_REF] Collett | Sequence learning by honeybees[END_REF][START_REF] Macquart | Sensimotor sequence learning in the ant Gigantiops destructor[END_REF] or to act accordingly to the cue they have just previously encountered [START_REF] Giurfa | The concepts of 'sameness' and 'difference' in an insect[END_REF][START_REF] Zhang | Visual working memory in decision making by honey bees[END_REF]. However, these feats requires many trials of experimental conditioning, and although some can be parsimoniously explained [START_REF] Cope | Abstract concept learning in a simple neural network inspired by the insect brain[END_REF], it remains unknown whether these insects spontaneously learn information about the sequence of views experienced along their familiar foraging routes [START_REF] Riabinina | Do wood ants learn sequences of visual stimuli?[END_REF].

In our study, ants were trained to home along a route through semi-natural terrain.

The route started with a short section through a channel providing a unique visual experience as ants begin their homeward journey. During tests, the familiar channel was moved to a different location at the middle of the route so that ants exiting the channel were exposed to a familiar view that is not the one they usually experience immediately after the channel. To assess whether breaking the normal sequence of views in such a way impacted the ants, the number of scanning behaviours that ants displayed after exiting the channel in several tests and control conditions was quantified. Scanning behaviour typically provides a proxy for assessing the ants' current navigational uncertainty [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF]. The results showed a strong effect of changing the sequence, which we discuss in the light of insect behaviour and neurobiology.

MATERIALS AND METHODS

Species and study site

All experiments were carried out on the desert ant Cataglyphis velox at a field site in the periphery of Seville, Spain. Cataglyphis velox is a thermophilic ant species common in the area that exhibits behavioural traits typical for desert ants [START_REF] Cerda | Behavioural and physiological traits to thermal stress tolerance in two Spanish desert ants[END_REF]. Instead of following pheromone trails, C. velox foragers venture out solitarily to search for food during the heat of the day and develop idiosyncratic routes relying on visual terrestrial and celestial navigational cues (Mangan andWebb, 2012, Schwarz et al. 2017).

General experimental set-up

Two experiments were conducted over two field seasons in June 2016 and June 2017. In both experiments ants were trained to run along a defined route to collect food items at a feeder location (Figs. 1A,C; 2A, B). The routes were enclosed by slippery white plastic planks (approx. 5 cm high) submerged in a 5 cm ditch. This enabled the ants to perceive the surrounding natural scenery during route-following while preventing them to forage elsewhere [START_REF] Wystrach | Ants might use different viewmatching strategies on and off the route[END_REF]. The foraging routes were cleared of clutter and vegetation to ease the movements of the foragers on the ground. Small plastic bowls (151515 cm) sunk into the ground so that their top edges aligned with the ground surface served as feeders. The upper rim of the feeder walls was covered with transparent tape to prevent the ants from escaping. Foraging ants eventually jumped or fell into the feeder and picked up a biscuit crumb or meal worm piece and were then individually marked with acrylic or enamel modelling paint. In both experiments, foragers that had picked up a food item started their homing journeys by travelling through an open-topped, 50510 cm white plastic channel that directly connected the feeder to the start of the homeward route. Thus, the visual route memories of all ants across conditions for the first 50 cm of their route was inside of the white channel. Only well-trained individuals with high familiarity of the visual surroundings were tested (see detail for each experiment below). For tests, homing ants were captured just before entering their nest so that their current path integration homeward vector (accumulated during the outbound trip) had returned to zero; hence termed zero-vector ants (ZV). For proper homing motivation, only ants holding a food item were tested. Once captured, the ant was transferred in a darkened plastic vial and released at one of the test locations along the route, either within a 'test channel' or directly on the ground (see details below). The transition between the capture point at the nest and the release at either the feeder or mid-route location caused an additional alteration of the view sequence and hence could trigger scanning behaviour. In all conditions, ZV ants were likely to scan a few times upon release from the carrying tube. However, ZV ants were always released 50 cm before the actual test areas, giving the foragers enough time and space to recover their bearings and resume visual homing before data recording started at the designated test areas (Figs. 1A, B; 2A). Furthermore, the test channel was always placed at the exact location where the ant homed during her previously displayed homing path. This procedure helped minimise changes in visual familiarity during tests. To avoid differences in the ground substrate across the different test locations the immediate area after the channel exit (5050 cm) was covered with a layer of sand (Figs. 1, 2; grey areas). In all tests, a GoPro Hero3+ camera was mounted on the top end of the test channel and the behaviour of the tested ant was recorded on the 50x50 cm area after the channel exit. Panoramic images shown in figures were taken with a Sony Bloggie camera and unwarped with PhotoWarp2.

Experiment 1

In June 2016 ants were trained to follow a curved outbound route to a feeder located approx. 8 m away from the nest and then a zigzagged shaped inbound route back to the nest (Fig. 1A). The homeward paths of ants started inside the plastic channel which had an approximate slope of 30 linking the entrance at the dug-in feeder to the channel exit at ground level (Fig. 1B). Hence the channel pointed up towards the sky and ants could see no terrestrial cues from inside. For each individually marked forager, training continued at least until they were able to negotiate a straight homebound route without colliding into the baffles or the surrounding planks enclosing the zigzag route (Fig. 1A). ZV ants were tested in one of the following conditions:

1. Test channel to feeder (start of the route). Ants were transferred into a test channel that was identical to the training channel and placed alongside the training channel (Fig. 1A) at the feeder. Thus, in the Feeder Test, the sequence of visual memories experienced was unaltered from training.

2. Test channel to mid-route. Ants were transferred into the same test channel as at the feeder but this time the channel was placed in the middle of the third leg of the zigzag route with the same compass orientation as at the Feeder (Fig. 1A).

The visual surrounding of the Mid-Route Test differed greatly from the one of the Feeder test as it contained several big, nearby artificial objects (Fig. 1A,C). Thus, this is the crucial experimental condition in which the sequence of familiar visual memories was altered as compared to a normal homing journey.

3. Novel channel to start or mid-route. A control for the potential difference in visual familiarity between the feeder-and mid-route release was the Novel Channel Test. Ants were transferred into an unfamiliar channel and released at either the feeder or the mid-route location (Fig. 1A). The novel channel was a modified version of the normal test channel. The walls and the ground were covered with thin beige cardboard and hence provided a different substrate material and colour. 4. Mid-route no channel (control). To control for the possibility that ants might always scan when released at the mid-route location, irrespective of the sequence alteration, a Mid-Route Control was conducted. The test channel was placed as for the Mid-Route Test location but the ants were released on the ground, right beside the beginning of the test channel (Fig. 1A).

Channel to unfamiliar location (control).

To verify that scanning behaviour is evoked by visual unfamiliarity, ants were released in the familiar test channel after it had been placed so that they would emerge in completely unfamiliar visual surroundings approx. 25 m away (Fig. S1).

Each ant was tested only once, in one of the test conditions.

Experiment 2

To account for individual variance in Experiment 1, a second experiment was conducted in June 2017 in which the same ant was tested in all conditions and therefore provided paired data across trials. Ants were trained to follow a straight foraging route (approx. 8 m long and 1.2 m wide; Fig. 2A) and similar training procedures to Experiment 1 were followed, whereby foragers always returned from the feeder via a 50 cm long channel before continuing their homebound trip. The training and test channels were augmented with black stripes attached to the walls to enhance visual contrast and optic flow as well as to lower potential reflections from the sun. An additional channel at the middle of the route (Mid-Route Test location) was present during all training trials (approx. 6 m in feeder-nest direction; Fig. 2A,B) to diminish differences of the views due to the test channel during Mid-Route Test and Mid-Route Control. Ants were individually marked and considered trained after performing at least five straight homing bounds. Trained ZV ants were subjected to the following test conditions.

1. Test channel to feeder (start of the route). In the Feeder Test, ants were released in a test channel (identical to the training channel) and placed alongside the training channel (Fig. 2A,B). As in the equivalent condition of Experiment 1, the sequence of views experienced by the ant was unaltered from training.

2. Test channel to mid-route. In the Mid-Route Test, ants were transferred to the same test channel as for Feeder Tests, but this time the channel was placed exactly on the location of the mid-route channel, thus replacing it (Fig. 2A,B). Here too, visual differences between Mid-Route-and Feeder Test location were emphasised by the additional visual objects (Fig. 2B). As in the equivalent condition of Experiment 1, the usual sequence of views experienced by the ant was thus altered.

3. Mid-route no channel (control). As in Experiment 1, for the Mid-Route Control ants were released on the ground, right beside the beginning of the mid-route channel (Fig. 2A,B) to test whether this location might appear less familiar than the Feeder location irrespective of the sequence.

4. Familiarity Control with altered visual surrounding at the Feeder Test was additionally conducted to test whether the increase of scans during Mid-Route tests could have been caused by a drop of familiarity just as the ants is exiting the test channel (Fig. S2). This exact view (at the border between the channel exit and the Mid-Route test surrounding) has never been encountered by the ants and might have triggered the scan increase in Experiment 1 and 2 instead of the altered sequence of views.

In Experiment 2, each ant was tested once in each condition, with at least two uninterrupted training trips between test. This provided individually paired data across the three tests. The order of tests varied across individuals in a systematic fashion.

Data recording and analysis

The number of scans performed by the ants on the 5050 cm test areas was assessed in two ways. Firstly, scans were observed and recorded directly in the field by two experimenters.

Scans were defined by the following criteria: the ant stops forward motion and rotates at least in one direction on the spot before resuming forward motion. Given the rotational component, such a behaviour is usually obvious and hence unambiguous. Experimenters agreed upon the number of scans on each test and the data-point was recorded. This was supported by video recording of all tests using a GoPro Hero3+ (19201080 pixel; 60 fps) and a Panasonic Lumix camera (DMC FZ200) for Fig. S2. Some video files were corrupted (Experiment 1: 17 out of 76; Experiment 2: 2 out of 66; Fig. S2: 12 out of 44) and the number of scans were solely based on live observations of two experimenters.

Differences between the number of scans across tests were analysed with a Generallinear-model (GLM) for count data (quasipoisson distribution). For Experiment 2 with paired data, we used the GLM for mixed effects with conditions as fixed effect and individual ants as random effect. In both experiments, the key Mid-Route Test condition -where the sequence of view is altered -was compared to both other conditions (Feeder Test and Mid-Route Control) simultaneously in the model.

RESULTS

To investigate whether recently experienced views affect the route following behaviour of ants, ZV ants were tested at the feeder (unaltered sequence of views) or at the middle of their familiar route (altered sequence of views) and the number of scans displayed in the area following the channel exit was analysed. The occurrence of scans is a suitable indicator of navigational uncertainty in this experimental context: ants exiting the test channel in totally unfamiliar surroundings showed systematic scanning behaviours (90%, 9/10) and the highest numbers of scans (up to 6) across all test conditions (Fig. S1).

Experiment 1

In Feeder Tests, that is without altered visual sequence, not a single ant (0%, 0/14) scanned in the test area (Fig. 1D). In contrast, in the Mid-Route Test, where ants experienced an altered visual sequence, 50% of ants (7/14) scanned at least once in the test area (Fig. 1D), indicating some degree of navigational uncertainty. There is a significant increase of scans in the Mid-Route Test when compared to the Feeder Test (GLM: P=0.026, Z=-2.357). In the Mid-Route Control, with the ants released beside of the mid-route-channel, only two out of twelve ants (16%) scanned (Fig. 1D) suggesting that the increased scanning number in the Mid-Route Test is not due to unfamiliarity of the absolute position, although this difference did not reach significance (GLM: P=0.215, Z=-1.272; Fig. 1D). An additional control confirmed that the increase of scans was due to the altered sequence of views from test channel to the visual surrounding at the exit of the test channel and not caused by a lack of visual route knowledge. Ants from the Novel Channel Tests showed no significant difference between the feeder and mid-route release points (GLM: P=0.932, Z=0.097; Fig. 1E). Both tests bore unfamiliarity due to the novel test channel and produced scans in 50% (7/14) and 42% (6/12) of ants respectively.

Experiment 2

In Experiment 2, each ant was tested in all three conditions (Feeder Test, Mid-Route Test and Mid-Route Control), providing paired data accounting for individual differences. As before, in the Feeder Test few ants (14%, 3/22) scanned in comparison to 77% (17/22) of ants in the Mid-Route Test condition (Fig. 2C). Also, in accordance with data from Experiment 1, only 9% (2/22) of ants in the Mid-Route Control scanned, which is in line with data observed in the Feeder Test (Fig. 2C). We observed a significant increase in scans during the Mid-Route Test as compared to the Feeder Test (GLM: P=0.001, Z=-3.502) and Mid-Route Control (GLM: P=0.002, Z=-3.166). This effect was not due to a few ants scanning many times as most of the ants (31/44) displayed a higher number of scans in the Mid-Route Test (Fig. 2D) and only one single ant decreased her number of scans between the Feederand Mid-Route Test. The sequence across test conditions was balanced across individual and had no detectable effect on the results (GLM: P=0.463, Z=-0.734).

DISCUSSION

A reliable sign of navigational uncertainty in ants is the occurrence of scanning behaviour (Wystrach et al., 2019;[START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF]. In the current study, 90% of ants leaving a familiar channel from a feeder and finding themselves in a completely novel location exhibited repeated scanning (Fig. S1). This behaviour was used as an assay to investigate whether experiencing familiar views in an altered, novel sequence also produces uncertainty in ants, indicating that their memory of routes includes some information about the sequence of views experienced. If so it would challenge, or require augmentation of the current prevailing models of ant route memory. The main finding of this study is that an alteration of the sequence of views along a familiar route reliably increases the probability of a scanning response in ants.

Specifically, in experiments conducted across two field seasons and with different nests, ants were trained along a homing route that started with a 50 cm channel, providing a unique and well-controlled visual experience, before exiting into the open route surroundings which they followed home. During tests, trained ants were captured close to their nest (to prevent the use of PI) and released in an identical-looking test channel. Upon release, these foragers dashed along the correct homing direction and out of the channel showing that they recognised the familiar channel scenery. If they then found themselves close to the unaltered (training) Feeder Test location they scanned rarely if at all (0% and 14% of ants scanned). However, if the channel had been relocated to the middle of the route, creating an altered visual sequence, foragers emerging from the channel typically stopped and displayed one or two scans (Mid-Route Test, 50% and 77% of ants scanned) before resuming their normal motion and completing the route at their usual pace (Figs. 1D,2C). Mid-Route Control ants, released 50 cm before the test area beside the beginning of the test channel showed little scanning behaviour in the actual test area (Mid-Route Control, 16% and 9%; Figs. 1D,2C). On the other hand, using a novel channel tended to equally induce scans in both the feeder and mid-route locations (Novel Channel Test, 50% and 42%; Fig. 1E). Overall, results suggest that it is the change in sequence, rather than anything about the mid-route location or the displacement from the nest back to the route, that causes navigational uncertainty.

Interestingly, ants exiting an unfamiliar looking channel (Novel Channel Tests) also displayed a high number of scan. This suggest that the novelty component of the channel (new wall colour and substrate) extended from inside to outside the channel, putting forward the idea that the unfamiliarity experienced at a given moment may have a sustained impact on behaviour.

Alternative explanations to sequence encoding

The overall experimental designed aimed to contrast the hypothesis of sequence of views vs. previous models of ant navigation. Results in all conditions validate the a priori predictions of the use of view sequence, and thus favour this novel hypothesis. However, alternative explanations may also explain the results.

The channel may exert a motor constraint by forcing the ants to keep to a fixed straight path and perhaps stereotyped movements on exiting it. It could be argued that it is these motor components rather than the scene in the channel that contributes to generating the unmet expectation that leads the ants to scan when exiting the Mid-Route channel during tests. Several papers have shown sequential links between vision and motor behaviour [START_REF] Chittka | Sensorimotor learning in bumblebees: long-term retention and reversal training[END_REF][START_REF] Collett | Sequence learning by honeybees[END_REF][START_REF] Macquart | Sensimotor sequence learning in the ant Gigantiops destructor[END_REF][START_REF] Vowles | Maze learning and visual discrimination in the wood ant (Formica rufa)[END_REF][START_REF] Zhang | Maze Learning by Honeybees[END_REF]. However, all these studies examine whether seeing a particular visual pattern can prime a turn in one direction, rather than the reverse. Motor constraints should have been minimal in our case because the straight and fast paths of C. velox held no apparent differences within the channel or not, at least to the naked eye, but this idea may still be worth investigating in other contexts.

It should be also noted that both the ants from the Mid-Route Test and the Mid-Route Control walked 50 cm before testing occurred (Fig. 1A,2A). In both conditions, tested ants typically scanned upon release, that is, 50 cm before the test area. Scans before the test area were not recorded which in hindsight would have allowed further scrutiny about potential differences between test and control. Yet, in the actual test area most scans occurred during Mid-Route Tests and hardly ever during Mid-Route Controls (Fig. 1D,2C).

When ants emerge from the (familiar) channel into (familiar) mid-route surroundings, they must experience for a very brief moment a composite view (channel in the rear-view and mid-route scene in the frontal visual field) which must be unfamiliar. It may be argued that this short moment where the overall scene must appear unfamiliar induced the scanning response observed in the Mid-Route Tests. In a follow-up control experiment (Fig. S2), we altered the rear-ward portion of the view as ants emerged from the channel into the (expected) start of the route (Feeder Test) and found that ants indeed would scan at a similar rate to those exiting the channel in the (unexpected) middle of the route (Mid-Route Test). However, this alternative explanation remains uncertain for several reasons. First, the artificial change created in this follow-up experiment exerted a much longer-lasting visual unfamiliarity than the one experienced at the exit line of the test route channel (Fig. S2). Second, Feeder tests and Mid-Route Controls also contained elements of unfamiliarity because ants were released on a location slightly offset compared to their usual route, but this did not trigger scans. Finally, the test channels were lopsidedly dug into the ground so that ants could not see the visual surroundings before exiting the channel (Fig. 1B), and hence the unfamiliar composite view would be experienced for no more than a split-second when passing the exit line (Fig. S2). Short moments of unfamiliarity must be experienced regularly when ants navigate through grassy environments or new fallen debris (not to mention bystanding experimenters). Yet ants do not trigger scans in these cases (pers. observ. SS, MM, BW, AW). Visual recognition in grassy environments must be noisy and responding to brief drops in the familiarity signal would lead to regular stops and scans, which seem counterproductive in these rapid runners.

The possibility of learning sequences in ants has been explored before in several experimental contexts but the results were not clear-cut (Macquart et al., Riabinia et al., 2011;2008;[START_REF] Schwarz | Visual discrimination, sequential learning and memory retrieval in the Australian desert ant Melophorus bagoti[END_REF]). Bulletproof evidence for learning a sequence of views would probably require experiments in virtual reality, where the tested ants can be easily and instantaneously 'transferred' from one part of the route to another.

How could sequences of views be encoded in the insect brain?

A most 'peripheral' explanation to the encoding of information about the visual sequence would be that ants do not store static but dynamic views, that is, how the visual input is actually changing as they move forward. Altering the sequence of familiar views as we did here would produce a novel -and thus unfamiliar -dynamical visual input, hence triggering scanning behaviours. Past observations in ants cast doubt upon this hypothesis. First, during scanning behaviours, ants actually stop and pause, exposing the visual system to a static view of the world during a tenth of a second or so before resuming motion in a correct direction. This behaviour is particularly apparent in fast walking desert ants such as Melophorus bagoti [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF]. During learning walks ants display numerous scan-like pauses while leaving the nest [START_REF] Fleischmann | Ontogeny of learning walks and the acquisition of landmark information in desert ants, Cataglyphis fortis[END_REF][START_REF] Fleischmann | Species-specific differences in the fine structure of learning walk elements in Cataglyphis ants[END_REF][START_REF] Müller | Path integration provides a scaffold for landmark learning in desert ants[END_REF][START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF] or the feeder [START_REF] Judd | Multiple stored views and landmark guidance in ants[END_REF][START_REF] Nicholson | Learning walks and landmark guidance in wood ants (Formica rufa)[END_REF], suggesting that they do learn static views of the world. Moreover, dynamic views of the world would intrinsically encode information about absolute distances of object, but experiments altering object configuration show that ant searches are based on retinal overlap rather than absolute distance [START_REF] Graham | The influence of beacon-aiming on the routes of wood ants[END_REF][START_REF] Judd | Multiple stored views and landmark guidance in ants[END_REF][START_REF] Wehner | Visual spatial memory in desert ants, Cataglyphis bicolor (Hymenoptera: Formicidae)[END_REF], suggesting that the stored views are static rather than dynamic.

Third, recent experiments in C.velox (work in preparation) and other species [START_REF] Murray | The role of attractive and repellent scene memories in ant homing (Myrmecia croslandi)[END_REF] show that ants easily recognise familiar views when tethered to run on the spot on a spherical air treadmill [START_REF] Dahmen | Naturalistic path integration of Cataglyphis desert ants on an air-cushioned lightweight spherical treadmill[END_REF], thus proving that views can be recognised without the change produced by forward motion.

Alternatively, information about view sequence could be encoded in the mushroom bodies, which are thought to be the siege of visual memories for navigation [START_REF] Webb | Neural mechanisms of insect navigation[END_REF]. There are several hypotheses for how a succession of views could be encoded in the mushroom bodies. One is that recurrency in this circuit could be exploited for learning temporal sequences [START_REF] Arena | Modeling the insect mushroom bodies: Application to a delayed match-to-sample task[END_REF][START_REF] Cognigni | Do the right thing: neural network mechanisms of memory formation, expression and update in Drosophila[END_REF][START_REF] Grünewald | Morphology of feedback neurons in the mushroom body of the honeybee, Apis mellifera[END_REF][START_REF] Li | Multimodal efferent and recurrent neurons in the medial lobes of cockroach mushroom bodies[END_REF]. Another is that connections between Kenyon cells (KC) could adapt through Hebbian mechanisms to alter the responsiveness to repeated pattern sequences [START_REF] Nowotny | Decoding temporal information through slow lateral excitation in the olfactory system of insects[END_REF]. Further, it is known that KCs possess several gap junctions between each other [START_REF] Wu | Heterotypic gap junctions between two neurons in the Drosophila brain are critical for memory[END_REF], suggesting that each active KC could increase the activation probability of other KCs, given a small delay. Under this assumption, the pattern of KCs activity at a given time is not only dependant of the current stimulus but also the previously active pattern of KCs, that is, the stimulus previously experienced [START_REF] Nowotny | Decoding temporal information through slow lateral excitation in the olfactory system of insects[END_REF].

Ultimate considerations

The current study suggests that the disturbance of one transition along the sequence affects the behaviour. This can be accounted by the storage of a short-sequence and does not necessarily imply that the complete sequence of experienced views is stored. From a computational perspective there are potential advantages in storing even short sequences of view memories, as it can reduce the risk of aliasing errors [START_REF] Graham | Insect navigation: do ants live in the now?[END_REF].

Matching of short sequence images has been shown to be very robust in robot localisation algorithms, even with drastic changes in the lighting such as sunny days vs. stormy nights [START_REF] Milford | SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights[END_REF], using very low resolution images [START_REF] Milford | Vision-based place recognition: how low can you go? The Int[END_REF], or with substantial tilt and pitch variation [START_REF] Stone | Skyline-based localisation for aggressively manoeuvring robots using UV sensors and spherical harmonics[END_REF]. Robustness to visual change and reduction of memory load would obviously be beneficial for ants that need to memorise and recognise long visual routes across their lifetime. Lastly, it is worth mentioning that mechanisms for visual navigation and the neural underpinning of visual memories seem to be shared across insects or at least across central place foraging hymenoptera [START_REF] Cheng | Arthropod navigation: ants, bees, crabs, spiders finding their way[END_REF][START_REF] Warrant | Visual navigation in nocturnal insects[END_REF][START_REF] Webb | Neural mechanisms of insect navigation[END_REF][START_REF] Wehner | Visual navigation in insects: coupling of egocentric and geocentric information[END_REF][START_REF] Zeil | The learning walks of ants (Hymenoptera: Formicidae)[END_REF]. Hence, it is likely that the influence of the sequences of views during route-following is not only limited to C. velox but also present in other ants and visually guided insects.

CONCLUSION

This study shows that altering the usual sequence of views triggers a transient resurgence of scanning behaviours even though the ants are still in their familiar environment.

Functionally, learning sequences of views might improve the robustness of visual recognition to environmental change. The experimental manipulations required to altered the sequence of views in the real world will always enable alternative explanations to be put forward.

Hence, future experiments using virtual reality could provide the means to a definite proof and the way to explore the mechanisms underlying visual sequence learning, which is likely to be widespread among insect navigators.

Fig. 1 .

 1 Fig. 1. Experiment 1. (A) Schematic aerial view of the experimental set-up with training and testing conditions. Ants were limited to follow a one-way foraging route between the nest and feeder. The feeder was connected to a channel that all ants had to pass to before they could return back to the nest along a zigzag-shaped inbound route. During tests zero-vector ants were transferred to one of three release points (Feeder Test, Mid-Route Test, Mid-Route Control) and their scanning behaviour was recorded in the designated test areas (grey quadrants). Dashed arrows indicate example paths of training and testing and black lines within the route depict baffles. (B) Schematic of test channel and data recording. All channels in training and testing were lopsidedly placed onto the ground with an approx. 30 slope. Tested ants were released in the channel and their subsequent scanning behaviour in the test area (grey quadrant) was recorded with a small camera at the top end of the channel. (C) Photographs of the experimental set-up with panoramic images from within the test channel, the Feeder-(unaltered view sequence) and the Mid-Route view (altered view sequence). Dashed line framing the set-up indicates the part of the route used in the experiment. (D) Results of the Feeder Test, Mid-Route Test and Mid-Route Control. Ants scanned significantly more at the Mid-Route Test as compared to the Feeder Test (GLM: P=0.026, Z=-2.357) but did not reach a significant difference when compared to Mid-Route Control (GLM: P=0.215, Z=-1.272). (E) Results of the Novel Channel Test. Ants showed no difference in scanning behaviour between Feeder and Mid-Route release points (GLM: P=0.932, Z=0.097).

Fig. 2 .

 2 Fig. 2. Experiment 2. (A) Schematic aerial view of the experimental set-up with training and testing conditions. Ants were trained to forage on a route between the nest and feeder. The feeder was connected to a channel that all ants had to pass to before they could return back to the nest. During tests zero-vector ants were transferred to one of three release points (Feeder Test, Mid-Route Test, Mid-Route Control) and their scanning behaviour was recorded in the designated test areas (grey quadrants). Dashed arrows indicate example paths of training and testing and black lines within the route depict baffles. (B) Photographs of the experimental set-up with panoramic images from within the test channel, the Feeder-(unaltered view sequence) and the Mid-Route view (altered view sequence). (C) Results of the Feeder test, Route test and Route Control. Each ant was tested at all three release points. Ants scanned significantly more at the Mid-Route release as compared to the Feeder (GLM: P=0.001, Z=-3.502) and Mid-Route Control (GLM: P=0.002, Z=-3.166) release points. (D) Increase of scans of individual ants compared between Feeder and Mid-Route release as well as Mid-Route Control and Mid-Route release points.

Acknowledgement. We thank Xim Cerda and his team at CSIC Seville for logistical and administrative assistance and provision of storage space. We also thank Cornelia Buehlmann and Paul Graham for discussions on early experimental design, Matthew Collett for feedback and Leo Clement, Evripidis Gkanias, Christelle Gassma and Cornelia Buehlmann for help and support during data collection and field preparation. We appreciate the assistance of Cristian Pasquaretta with GLM analyses. Finally, we thank Tom Collett and two anonymous reviewers for their helpful comments during review process.

Ethics. This work did not require any ethical approval. Ants were free to forage and return to the nest at will in our set-up. Only self-motivated foragers (holding their cookie crumbs) were tested. No ants were killed.

Funding. The study was partly financed by the European Research Council, 759817-EMERG-ANT ERC-2017-STG and by the Engineering and Physical Sciences Research Council EP/M008479/1.

Data availability. All data generated or analysed during this study are included in this published article (and its Supplementary information).


Antoine Wystrach
email: antoine.wystrach@univ-tlse3.fr

Cornelia Buehlmann

Sebastian Schwarz

Ken Cheng

Paul Graham

Rapid Aversive and Memory Trace Learning during Route Navigation in Desert Ants

published or not. The documents may come

Summary

The ability of bees and ants to learn long visually guided routes in complex environments is perhaps one of the most spectacular pieces of evidence for the impressive power of their small brains. While flying bees can visit flowers in an optimised sequence over kilometres, walking solitary foraging ants can precisely recapitulate routes of up to a hundred metres in complex environments [START_REF] Collett | Spatial memory in insect navigation[END_REF]. It is clear that route following depends largely on learnt visual information and we have a good idea how visual memories can guide individuals along them [START_REF] Baddeley | A model of ant route navigation driven by scene familiarity[END_REF][START_REF] Zeil | Visual homing: an insect perspective[END_REF][START_REF] Collett | A desert ant's memory of recent visual experience and the control of route guidance[END_REF][START_REF] Wystrach | Ants might use different view-matching strategies on and off the route[END_REF][START_REF] Schwarz | How Ants Use Vision When Homing Backward[END_REF], as well as how this is implemented in the insect brain [START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF][START_REF] Webb | Neural mechanisms of insect navigation[END_REF]. However, little is known about the mechanisms that control route learning and development. Here we show that ants (Melophorus bagoti and Cataglyphis fortis) navigating in their natural environments can actively learn a route detour to avoid a pit-trap. This adaptive flexibility depends on a mechanism of aversive learning based on memory traces of recently encountered stimuli, reflecting the laboratory paradigm of trace conditioning. The views experienced before falling into the trap become associated with the ensuing negative outcome and thus trigger salutary turns on the subsequent trip. This drives the ants to orient away from the goal direction and avoid the trap. If the pit-trap is avoided, the novel views experienced during the detour become positively reinforced and the new route crystallises. We discuss how such an interplay between appetitive and aversive memories might be implemented in insect neural circuitry.

RESULTS AND DISCUSSION

Ants can reshape their route to circumvent a trap.

We first let Australian solitarily foraging ants Melophorus bagoti shuttle back and forth between their nest and a feeder full of cookie crumbs located 5 m away. For the outbound trip, the ants had to walk through a long and narrow channel suspended 15 cm above the ground that connected the nest directly to the feeder. For the way back to the nest, ants loaded with a cookie crumb were free to navigate on the desert ground. After a day of shuttling back and forth, all marked ants had established a fairly direct homing route to the nest (Figure 1Ai). We then opened a pit-trap, previously buried inconspicuously into the desert floor, creating a 2 m long, 10 cm wide gap perpendicular to the nest-to-feeder route.

During their first homing trial with the trap, all trained ants ran as usual along the first part of the route and suddenly dropped into the trap. The trap was 10 cm wide and 10 cm deep (7  7 cm for Cataglyphis fortis) so that ants could see only the sky. The trap had slippery walls to prevent the ants from escaping and contained small twigs, which desert ants naturally tend to avoid as they impede walking. The trap offered a single possible exit formed by a 20 cm wide board (5 cm for C. fortis) leading from the base of the trap to the second part of the homeward route. The time the ants were trapped in the trap varied from one to tens of minutes, but once out, all individuals showed no apparent problem in returning directly to their nest (Figure 1Aii). We let ants shuttle back and forth with the trap open and recorded their paths again after 24h. After such training, several ants (4 out of 14, 29%) displayed a new route that circumvented the trap (Figure 1Aiii green trajectories). The tendency to detour the trap on the right side may due to two factors: 1-the nest relocated its entrance slightly on the right (as observed in [START_REF] Wystrach | Views, landmarks, and routes: how do desert ants negotiate an obstacle course?[END_REF]), so that the trap is not actually perpendicular to the feederto-nest beeline, making detours slightly shorter to the right than to the left; 2-a high cluttered region stands on the left of the setup, and desert ant species tend to be repelled by region presenting a high skyline [START_REF] Heusser | The visual centring response in desert ants, Cataglyphis fortis[END_REF][START_REF] Schwarz | Desert ants (Melophorus bagoti) navigating with robustness to distortions of the natural panorama[END_REF]. We replicated these experiments at a larger scale (8 m route and 4 m wide trap) with North African desert ants from Tunisia (Cataglyphis fortis) and obtained similar results (13 out of 47, 28% avoided the trap after 24h) (Figure 1Bi,ii,iii). Note that black objects were added around the experimental setup to visually enrich the barren C. fortis environment (see STAR METHODS).

Why some ants did not learn to circumvent the trap may be due to different reasons. A good proportion of those ants in both species did show modification of their routes by learning to avoid the trap using alternative strategies such as jumping directly onto the exit stick (see red paths in SI1), or simply learning a quick route through the trap by systematically falling in at the same spot and quickly reaching the exit stick with very little search (see Ant 7 in SI1).

Finally, some ants simply did not learn, perhaps because they performed too few training trials within the 24h period. Our personal observations were that the specifics of how ants hit the pit-trap influenced their ultimate strategy. Some ants fell by chance near the exit bridge and so spent less time in the trap itself. These ants were more likely to develop a strategy that depended on hitting the trap, but also knowing how to get out quickly. Future experiments could investigate the possibility that a longer time of being trapped yields stronger aversive memories. In any case, a simple categorisation of whether the ants circumvented the trap or not is sufficient to show that such an effect is unlikely to happen by chance (1st_trial_with_trap vs. 24h_with_trap: N(circumvented) /N(all ants) : 0/30 vs. 17/61, Fisher's Exact test p<0.001), and that there was no apparent difference in detour success between species (24h_with_trap M. bagoti vs. C. fortis: N(circumvented) /N(all ants) : 4/14 vs. 13/47, Fisher's Exact test p=1).

New routes are based on learnt terrestrial cues.

Desert ants are well known to follow habitual routes guided by learnt terrestrial cues although they also have access to their Path Integration (PI) system at all times [START_REF] Knaden | The sensory ecology of ant navigation: from natural environments to neural mechanisms[END_REF][START_REF] Collett | Path integration: how details of the honeybee waggle dance and the foraging strategies of desert ants might help in understanding its mechanisms[END_REF], a navigational strategy that is particularly pronounced in C. fortis [START_REF] Cheng | Beginnings of a synthetic approach to desert ant navigation[END_REF]. We carried out several manipulations to ask whether learnt terrestrial cues, rather than PI, were controlling the new routes of our foragers.

Ants captured just before entering their nest and then re-released at the feeder are called zero vector (ZV) ants because their PI state is zero at the onset of homing, and thus no longer provides correct homeward information. Such ZV ants that had circumvented the trap during their previous (full vector, FV) run were equally successful in their subsequent ZV run (Figure 1Biv) whereas ants that had fallen into the trap as FV ants still did so as ZV ants (previous_FV circumvented vs. previous_FV fell: N(circumvented) /N(all ants) : 12/12 vs. 2/13, Fisher exact test p<0.001), showing that guidance along the newly learnt route does not require PI.

Interestingly, ZV ants displayed turns before the trap even if they had failed as FV, showing that the learning process itself has to do with views rather than the ant's PI state. Note also that 2 out of 13 ants fell as FV but avoided the trap as ZV, which further supports the hypothesis of visual route learning. We know that the directional dictates of PI and learnt visual guidance are integrated, even when pointing in different directions [START_REF] Collett | How Navigational Guidance Systems Are Combined in a Desert Ant[END_REF][START_REF] Wystrach | Optimal cue integration in ants[END_REF][START_REF] Legge | Combining sky and earth: desert ants (Melophorus bagoti) show weighted integration of celestial and terrestrial cues[END_REF][START_REF] Wehner | Steering intermediate courses: desert ants combine information from various navigational routines[END_REF]. Thus, in FV ants, the PI vector points directly to the nest and thus may bias the path towards the trap.

Therefore, the paths of ZV ants are more representative of the route as guided by terrestrial cues. Nevertheless, the extent to which PI information may be associated with aversive experiences should be more fully studied.

We further tested whether ants that had learnt a new route to circumvent the trap would still follow it, if the trap was removed again. Five out of the six individuals tested displayed the usual detour even though the trap had been removed (Figure 1Aiv; No_trap_initially vs. trap_removed_again: N(circumvented) /N(all ants) : 1/20 vs. 5/6, Fisher exact test p<0.001). This confirms that the detour does not depend on perceiving the trap, but on route memories.

Finally, we recorded a cohort of ants that had started their foraging life while the trap was already in place. We did not control how many trials each ant produced but within a period of 24h we observed that several individuals learnt routes that circumvented the trap (Figure 1Av). The proportion of ants that circumvented the trap was similar between these ants and ants that had some route experience before the trap was set in place (24h_with_trap_naive vs. 24h_with_trap: N(circumvented) /N(all ants) : 5/15 vs. 4/14, Fisher exact test p=1). This shows that a route that circumvents a hidden trap will develop naturally, whether the trap is novel or has been there for all of an ant's foraging career.

The use of chemical trails, scent marks or other social information would be unlikely in these highly visual solitary foraging ants, however we can further definitively rule them out, by observing the typical idiosyncrasies of ants' individual routes [START_REF] Collett | Visual landmarks and route following in desert ants[END_REF][START_REF] Kohler | Idiosyncratic route-based memories in desert ants, Melophorus bagoti: How do they interact with path-integration vectors?[END_REF][START_REF] Mangan | Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox)[END_REF]: even though trials were interleaved between individuals and spanning over different days, routes are remarkably similar across subsequent trials of a same individual (Figure 2A, Figure S1) but they vary substantially across individuals (Figure 1, Figure S1). We can be confident that ants were not using social information, but private information based on learnt terrestrial cues. The nature of these learnt terrestrial cues is not crucial for our purpose here, but based on past evidence with desert ants, which run at high speed (>50cm/s) with the head and antenna lifted upwards, we can be confident that it is mostly based on visual cues [START_REF] Wystrach | Views, landmarks, and routes: how do desert ants negotiate an obstacle course?[END_REF][START_REF] Collett | Visual landmarks and route following in desert ants[END_REF][START_REF] Wehner | The architecture of the desert ant's navigational toolkit (Hymenoptera: Formicidae)[END_REF], typically the recognition of familiar panoramic views [START_REF] Zeil | Visual homing: an insect perspective[END_REF][START_REF] Graham | Which portion of the natural panorama is used for viewbased navigation in the Australian desert ant?[END_REF][START_REF] Wystrach | Landmarks or panoramas: what do navigating ants attend to for guidance[END_REF]. To ease the reading, we will now refer to these learnt terrestrial cues as 'familiar views'.

How do ants reshape their routes? Evidence for aversive and memory trace learning.

To shed light on the processes that lead from an established route to a new route that circumvents the trap, we tracked all successive trials of individually marked M. bagoti ants from the first time they encountered the trap onwards. In addition to their paths, we recorded the locations where ants stopped and scanned their surroundings. Scanning is a typical behaviour in desert ant navigators (and particularly obvious in M. bagoti): the ant suddenly stops and rotates on the spot, pausing in different directions successively [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF]. Scans are triggered when an ant experiences a decrease in visual familiarity [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF], when multiple directional cues are put into conflict [START_REF] Wystrach | Multiple sources of celestial compass information in the central Australian desert ant Melophorus bagoti[END_REF][START_REF] Wystrach | Running paths to nowhere: repetition of routes shows how navigating ants modulate online the weights accorded to cues[END_REF], when running a route has not resulted in success [START_REF] Collett | A desert ant's memory of recent visual experience and the control of route guidance[END_REF][START_REF] Wystrach | Running paths to nowhere: repetition of routes shows how navigating ants modulate online the weights accorded to cues[END_REF], or simply when naïve ants exit their nest for the first time [START_REF] Fleischmann | Species-specific differences in the fine structure of learning walk elements in Cataglyphis ants[END_REF][START_REF] Jayatilaka | The choreography of learning walks in the Australian jack jumper ant Myrmecia croslandi[END_REF][START_REF] Müller | Path Integration Provides a Scaffold for Landmark Learning in Desert Ants[END_REF]. In other words, the occurrence of scanning reflects a directional uncertainty in an ant's navigational system [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF].

As described above, on the first run with the trap in place, ants rush along their direct homeward route and fall into the trap. In subsequent trials, routes of most ants changed and some eventually circumvented the trap. We supposed two potential mechanisms. First, it could be that the negative event of falling into the trap triggers learning oriented behavioural routines that occur immediately after the negative event. This was not the case. When ants emerged from the trap, they rushed towards their nest as usual, and displayed neither more scanning or meandering than before the trap was set (Second part of the route: Trial before trap vs. Trial with first trap fall: mode = 0 scan/ant in both cases, glme #Scan: t=0.359 p=0.721; glme meander z=0.027 p=0.978; Figures 2Bi and2Bii, see also Figures 1Aii andBii)).

Second, ants could keep memory traces of the scenes experienced immediately before falling into the trap, and change the valence of these recent memories given the current negative experience of being trapped. In our paradigm, this would predict that ant behaviour will be affected on the run subsequent to falling in the trap when in the area immediately before the trap. Indeed, this is what we observed. Ants displayed a clear increase in scanning behaviours in the region before the trap (mode and median = 2 scans/ant, Figure 2Biii), significantly more than they had on their previous run (mode and median = 0 scan/ants, Figure 2Bii Mechanistically, learning must be based on a memory trace because the US (i.e., being in the trap) is experienced after the CS (i.e., the view of the surrounding scenery), and crucially, with no time overlap: for being in the trap (US) prevents entirely the view of the scenery (CS), and the trap is invisible from outside. Being exposed to the CS before the US with no time overlap between both stimuli reflects the laboratory paradigm of 'trace conditioning' [START_REF] Bouton | Learning and behavior: A contemporary synthesis[END_REF]. How long is the delay between CS and US cannot be determined in our natural situation, although the apparent increase of scanning behaviours up to 2 metres before the trap (Figures 2A,2C) suggests that the memory trace of the views may be kept for at least a couple of seconds. The resulting aversive memory formed, however, lasts across days to trigger salutary avoidance and scanning behaviours (Figure 2A, 'day2' grey arrow).

Aversive trace learning also provides an explanation for past results: ants repeatedly captured at the same location and asked to repeat their visually guided route display turns and avoidance behaviours before the capture point after a couple of trials only [START_REF] Wystrach | Running paths to nowhere: repetition of routes shows how navigating ants modulate online the weights accorded to cues[END_REF]. Being captured likely provides a negative US and the views preceding the capture point become aversive. This happens even though the ants were never allowed to reset their Path Integrator by being put back to the nest [START_REF] Knaden | Ant navigation: resetting the path integrator[END_REF], showing that such long term aversive memories can be formed rapidly and without the need of the PI system.

Neural implications.

We now have a good idea of the neural underpinnings of learning in insects from studies of the Mushroom Bodies (MB) [START_REF] Cohn | Coordinated and Compartmentalized Neuromodulation Shapes Sensory Processing in Drosophila[END_REF][START_REF] Bouzaiane | Two Independent Mushroom Body Output Circuits Retrieve the Six Discrete Components of Drosophila Aversive Memory[END_REF][START_REF] Caron | Neuroscience: Intelligence in the Honeybee Mushroom Body[END_REF][START_REF] Withers | Experiencedependent plasticity in the mushroom bodies of the solitary bee Osmia lignaria (Megachilidae)[END_REF][START_REF] Devaud | Neural substrate for higher-order learning in an insect: Mushroom bodies are necessary for configural discriminations[END_REF], which are assumed to be the seat of the route visual memories [START_REF] Webb | Neural mechanisms of insect navigation[END_REF][START_REF] Ehmer | Segregation of visual input to the mushroom bodies in the honeybee (Apis mellifera)[END_REF][START_REF] Stieb | Visual Experience and Age Affect Synaptic Organization in the Mushroom Bodies of the Desert Ant Cataglyphis fortis[END_REF] (Figure 3). Each view experienced can be represented by a specific pattern of activation of Kenyon Cells (KC) in the MB [START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF], and KCs project onto multiple output neurons (MBONs) conveying different valences [START_REF] Aso | Mushroom body output neurons encode valence and guide memory-based action selection in Drosophila[END_REF][START_REF] Aso | Dopaminergic neurons write and update memories with cell-type-specific rules[END_REF]. By modulating the synaptic weights between the active KCs and specific MBONs, views can be thus be associated with positive or negative valences, presumably inciting forward movements [START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF][START_REF] Webb | Neural mechanisms of insect navigation[END_REF] or turns respectively (see also [START_REF] Möel | Opponent processes in visual memories: a model of attraction and repulsion in navigating insects' mushroom bodies[END_REF]).

To accommodate these principles of MB function our results simply require that 1-a trace (or tag) must remain in the KCs neurons (or their output synapses) for at least a few seconds after they have fired; and 2-A negative event (such as being trapped) must be able to change the valence associated with the tagged neurons, so that familiar views perceived before falling into the trap become less positive or aversive (Figure 3).

How and where exactly the tagging may happen remains unclear as several types of modulatory neurons project to these mushroom body compartments [START_REF] Perisse | Associative memory: without a trace[END_REF][START_REF] Liu | The GABAergic anterior paired lateral neuron suppresses and is suppressed by olfactory learning[END_REF]. Finally, if it is now clear that being trapped or caught can constitute a negative reinforcement, what constitutes positive reinforcement during route learning is still unclear: it could be reaching the nest or running down the PI accumulated home vector [START_REF] Ardin | Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments[END_REF]. Such positive reinforcement might also involve the 'tagged neurons', so that not only the current but also the previously experienced views that led to a positive outcome become associated to a positive valence. In any case, it is clear that route reshaping here results from an interplay between avoidance behaviour triggered by memory trace learning and appetitive learning based on positive reinforcement (Figure 3).

CONCLUSION

We have demonstrated here how memory trace learning is adaptively applied to navigation in natural habitats, where a negative experience labels specific locations in space that precede the aversive event. Behaviourally, this allows an ant to solve a navigational problem by efficiently reshaping its route through the world. Such route plasticity thus results from an interesting interplay between aversive and appetitive visual memories, and between avoidance learning (a form of negative reinforcement) and positive reinforcement, which matches well our current understanding of insect learning circuits.

Main-text figure legends

Figure 1. Negative experience shapes ants' routes. A. Australian desert ants (Melophorus bagoti) or B North African desert ants (Cataglyphis fortis) were followed for a series of homeward routes from a permanent feeder (F), with a pit-trap in place (solid line) or covered over (dashed line). Two small dashes across the trap line indicate the escape board. Green paths from ants that circumvent the pit-trap and blue paths from ants that fall into the trap.

(i) Control routes of ants between feeder and nest, without trap (A, n=20; B, n=16). (ii) The first route after the installation of the pit-trap (A, n=15; B, n=15). (iii) Paths after the pit-trap has been in place for 24 hours (A, n=14; B, n=47). (iv) and (v) show different conditions for the two species. (Aiv) paths of M. bagoti ants that had previously learnt to circumvent the trap, tested with the trap covered (n=6). (Av) the paths of ants that had begun their foraging life (i.e., since naïve stage) with the trap in place and had 24 hours of foraging experience (n=15).

(Biv, v) paths of C. fortis ants tested as ZV ants (without useful path integration information, as the path integrator no longer points towards the nest, but towards the feeder) that had previously succeeded (Biv, n=12) or failed (Bv, n=13) to circumvent the trap as full-vector ants.

'n' refers to the number of ants tested. (modified from [START_REF] Stieb | Visual experience affects both behavioral and neuronal aspects in the individual life history of the desert ant Cataglyphis fortis[END_REF]) C. Mushroom-Bodies neural architecture derived from the insect literature [START_REF] Webb | Neural mechanisms of insect navigation[END_REF][START_REF] Heisenberg | Mushroom body memoir: from maps to models[END_REF]. Visual information is sparsely projected via visual Projection Neurons (PNs) to the Kenyon Cells (KCs). KC activity thus represents views that can be associated with the Mushroom Body output neurons (MBONs) mediating appetitive or aversive valences. Such associations result from the modulation of KC-to-MBON synapses; the modulation is generated by the co-activation of KCs and dopaminergic neurons (DANs) relaying the valence of the current situation. The resulting balance between aversive and appetitive MBON activities can then control steering. D. The current study suggests memory trace learning as a mechanism to explain the reshaping of routes. First, a trace of the KC activity must be kept for a few seconds (top panel). Second, the co-activation of a dopaminergic neuron modulates the KC-to-MBON synapses of these recently activated KCs (bottom panel). Note that modulation consists of a synaptic depression, which explains why DANs of a given valence modulate MBONs of the opposite valence. Thus an aversive situation, such as being trapped, will be mediated by a DAN decreasing the connection strength between the recently activated KCs and the appetitive MBON. These KCs will no longer activate the appetitive MBON, but still activate the aversive MBON. In other words, the view experienced before the trap will henceforth trigger an aversive response.

STAR Methods

LEAD CONTACT AND MATERIALS AVAILABLITY

Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Antoine Wystrach (antoine.wystrach@univ-tlse3.fr). This study did not generate new unique reagents.

EXPERIMENTAL MODEL AND SUBJECT DETAILS

Two species were tested in this study: the Australian red honey ant, Melophorus bagoti and the Saharan desert ant Cataglyphis fortis. Both species are highly thermophilic ants (Wehner, 1987 By using two species of desert ants that belong to different phylogenetic tribes, we hoped to investigate the generality of this route re-learning process.

METHOD DETAILS

Experimental set-ups

The experimental set-ups for the two species were similar, with a larger scale for C. fortis to reflect their typically longer range of foraging (up to hundreds of meters for C. fortis and up to 70 m in M. bagoti, personal observation) [START_REF] Cheng | Beginnings of a synthetic approach to desert ant navigation[END_REF]. Measurements below are given for M. bagoti, followed by C. fortis in brackets. Experiments were undertaken with a nest located in an area cleared of grass but surrounded by bushes and trees (or artificially added large black cylinders for C. fortis) providing rich visual information for navigation.

In both experimental set-ups, ants moved between their nest and a feeder full of cookie crumbs 5 m (8 m for C. fortis) away. The ants' nest was covered with an overturned bucket that had the bottom removed, whose opening at ground level was connected to a straight outbound channel (5 m long, 10  10 cm for M. bagoti; 8 m long, 7  7 cm for C. fortis) elevated outbound channel suddenly ended above the feeder, into which ants would drop. The feeder was a small plastic container sunk into the ground providing biscuit crumbs ad libitum. To return to the nest, the ants climbed out of the feeder using a small ramp and walked on the desert ground back to the nest. Removing the feeder ramp at critical times allowed us to control which ants ran their homeward journey and when. Halfway along their homing route, a plastic channel was buried inconspicuously into the desert floor, creating a 2 m long, 10 cm wide (4 m long 7 cm wide for C. fortis) trap perpendicular to the nest-to-feeder route. The trap was buried entirely so as to remain invisible to the ants. The trap had smooth walls and was filled with twigs to hinder ant movement. Ants could leave the trap by using a single exit formed of a stick bridge 20 cm wide (5 cm for C. fortis), connecting the bottom of the trap to the second part of the route. A grid of lines (mesh width: 1 m) was set up by winding strings around pegs (or by painting on the ground with C. fortis) and the ants' homing paths before and after introducing the trap were recorded on squared paper. During initial training the trap was covered by a thin board, with desert sand scattered on top, so that the ants could shuttle unimpeded.

Experimental protocols

For both species, ants that arrived at the feeder were marked with a dot of day-specific enamel paint to ensure that ants were experienced before the trap was set. Only ants with at least 24 hours experience were recorded. Once the trap was set, the ants' first homing paths after trap introduction were recorded as well as their paths 24 hours later.

With C. fortis a group of ants was recorded twice. Here, the ants performed their homing route and just before they entered the nest they were taken and released again at the feeder as zero-vector ants, to test whether PI could provide an alternative explanation to views. C. fortis is well suited for this control as this species is known to strongly rely on PI (compared to M. bagoti) [START_REF] Cheng | Beginnings of a synthetic approach to desert ant navigation[END_REF].

With M. bagoti, an additional treatment was enacted. Successful ants that circumvented the trap were marked and, once they return to the feeder again, tested with the trap covered again (as in the initial training).

Another condition was tested with naïve M. bagoti ants. The trap was set in place and all ants were marked for 5 consecutive days. After this period, all unpainted ants reaching the feeder were considered 'naïve' and were painted with a specific colour. Naïve ants were free to forage for 24 hours before being recorded.

Finally, some M. bagoti ants were marked with individual colour codes in order to obtain a record of the evolution of individual routes. In this treatment, we recorded both the path and the occurrence of the clear cut scanning behaviours typically observed in this species [START_REF] Wystrach | Visual scanning behaviours and their role in the navigation of the Australian desert ant Melophorus bagoti[END_REF].

QUANTIFICATION AND STATISTICAL ANALYSIS

Paths were digitised using the software Graphclick. Meander was calculated as the mean angular deviation in direction between successive 30 cm chunks of the ants' paths. For the 'Avoid vs. fell comparison' we used Fisher's Exact Test to look for differences between groups in the ratio of ants that circumvented or fell into the trap. For the 'Scan number and meander' comparisons ants were followed individually across successive trials. We compared scan number and meander across three situations: (i) Trial before trap; (ii) Trial with first trap-fall;

(iii) Trial following first trap-fall for two sections of the route, before the trap and after the trap, separately. To analyse the number of scan per ants, we used a generalised linear mixed effects model with ants as a random effect for count data. Given that the dispersion parameter (null deviance / df = 2.67 for 'first part of the route' and 2.08 for 'second part of the route') is above 2, we used a quasiPoisson distribution rather than a Poisson distribution (glmmPQL from MASS library in R). For meander values, we scaled the data between 0 (Meander = 0deg) and 1 (Meander = 180deg) and used a generalised linear mixed effects model for proportional data with ants as a random effect (family="beta", link="probit" ; glmmTMB library in R). Successive routes of Melophorus bagoti individuals from the first run incorpora�ng the trap onwards. Scan loca�ons are marked with a circle and routes are colour coded as in Figure 1 and 2 with the addi�on of paths marked in red for ants that learnt to use the s�ck bridge efficiently.

  ) before falling into the trap for the first time (First part of the route: Trial with first trap fall vs. Trial following first trap-fall: glme #Scan: t=4.186 p<0.001). Similarly, their paths showed significantly more meandering as they approached the trap for the second time compared to their previous run (First part of the route: Trial with first trap fall vs. Trial following first trapfall: glme meander: z=3.006 p=0.003).

Figure 2 .

 2 Figure 2. Negative experience modifies the memory of the views experienced before the trap. Individually marked ants of Melophorus bagoti were tracked for a sequence of runs before and after the activation of the pit-trap. A. Sample sequence from a single ant. Locations where the ant stops and scans the world are marked with a circle. As elsewhere in the paper, paths are coloured coded: blue for ant paths that fall into the trap and green paths that avoid the trap. See Figure S1 for more individual examples. B. For each ant the number of scans was recorded before (bottom, blue) and after (top, red) the trap across three focal trials. (i) Trial before the trap was activated; (ii) Trial with first trap-fall; (iii) Trial following first trap-fall. From this we calculated the probability of an ant scanning (N(ants scan>0)/N(all ants)), and the median number of scans, for each trial and route segment. Before the trap: (i) N=31 proba=0.45 Median=0 (ii) N=25 proba=0.40 Median=0 (iii) N=25 proba=0.80 Median=2. Star indicate a significant increase in scan number (see text for detail). After the trap: (i) N=31 proba=0.35 Median=0 (ii) N=25 proba=0.40 Median=0 (iii) N=25 proba=0.32 Median=0. C. For the same three focal trials, the location of scans is shown relative to the Feeder (F, (0,0)), Nest

Figure 3 .

 3 Figure 3. Overview of the appetitive and aversive learning experiences. A. illustrations of the concept. Across a sequence of journeys for a typical ant we show the regions of operation for aversive (red) and appetitive (green) learning. The aversive region is first formed by memory trace learning on the trials where the ant encounters the trap. Over time a detour develops, and the new route is maintained by appetitive processes. B. Picture of a Cataglyphis fortis brain's right hemisphere showing the optic lobes (OL) and Mushroom-Bodies (ML)

  ; Christian & Morton, 1992) that forage solitarily on dead insects and plant materials (Muser et al., 2005). Experiments with M. bagoti were achieved in January-March 2014, ~10 km south of Alice Springs, Australia, on the grounds of the Centre for Appropriate Technology, in a semi-arid desert habitat characterised by red soil, grass tussocks, bushes, and trees of Acacia and Eucalyptus species. Field experiments with C. fortis were performed in June-July 2015 in a flat salt pan (34.954897 N, 10.410396 E) near the village of Menzel Chaker, Tunisia.

2 Figure S1 .

 2S1 Figure 1

Acknowledgments

We would like to thanks Cody Freas and Jeanne Delor for help on the field, Cristian Pasquaretta for statistical advice as well as the Centre for Appropriate Technology Limited (CfAT Ltd, Alice Springs) for field facilities and letting us run experiments on their grounds. The research was funded by the European Research Council (ERCstg: EMERG-ANT 759817) to AW, a Fyssen Foundation fellowship to AW, the European Union's Seventh Framework Programme (FP7: PIEF-GA-2013-624765) to CB and a grant from the Australian Research Council to KC (DP110100608). PG was funded in part by EPSRC grant EP/P006094/1.

DATA AND CODE AVAILABILITY

All data are available at: https://github.com/awystrac/Rapid-aversive-and-memory-trace-learning_Current_Biol_2020/commits?author=awystrac

Author contributions

Conceptualisation

Declaration of interests

The authors declare no competing interests.

KEY RESOURCES TABLE

REAGENT or RESOURCE SOURCE IDENTIFIER

Experimental Models: Organisms/Strains

Melophorus bagoti

Field site near Alice Springs (Australia) N/A

Cataglyphis fortis

Field site near Menzel Chaker (Tunisia) N/A