Temporal and Spatial Pattern of Trail Clearing in the Australian Meat Ant, Iridomyrmex Purpureus

Animal Behaviour 150 (2019) 97e111

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Animal Behaviour

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Temporal and spatial pattern of trail clearing in the Australian meat ant, Iridomyrmex purpureus

* Eliza J. T. Middleton a, 1, Simon Garnier b, Tanya Latty a, Chris R. Reid c, , 1 a School of Life and Environmental Sciences, Faculty of Agriculture and Environment, The University of Sydney, Sydney, NSW 2006, Australia b Department of Biological Sciences, New Jersey Institute of Technology, Newark, NJ 07102, U.S.A. c Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia article info Many ant species use trails to connect important resources. In addition, some ants actively clear obstacles fi Article history: from their trails. Although trail clearing is thought to be bene cial in decreasing travel time, the physical Received 20 June 2018 process of clearing requires an investment of time and energy. Given that trail clearing is a decentralized Initial acceptance 14 August 2018 process, how do colonies decide when to invest in clearing? In this study, we examined trail clearing in Final acceptance 14 January 2019 the Australian meat ant, using artificial semipermeable barriers mimicking grass. We tested the hy- Available online 9 March 2019 pothesis that investment in clearing was influenced by the abundance and physical toughness of ob- MS. number: 18-00410R stacles and that the selection of which grass blade to cut was a nonrandom process that decreases travel distance. We found that low abundance/low toughness treatments experienced the greatest amount of Keywords: clearing and high abundance/high toughness the least. Although ants did not clear an optimally efficient Iridomyrmex purpureus trail, the results of our percolation analysis support the inference that the ants strategically deployed pheromone trail clearing, taking multiple factors into account when deciding to invest in this strategy. The resultant self-organization trail clearing clearing patterns provided shorter travel routes for foraging ants than would be expected by the random removal of obstacles. © 2019 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Many group-living animals rely on transport systems such as Acosta, Lopez, & Serrano, 1993; Acosta, Lopez, & Serrano, 1995; trails, burrows or galleries to facilitate efficient and safe movement Plowes, Johnson, & Holldobler,€ 2013), Camponotus (see; Marlin, of resources, individuals and information through the environment. 1971), Iridomyrmex (see; Greaves & Hughes, 1974) and Acro- Despite having small brains and a lack of centralized control, trail- myrmex (see; Gamboa, 1975; Wetterer, 1995). laying ants manage to build networks that solve shortest path Trail clearing allows for more efficient habitat exploitation by problems (Latty et al., 2011; Reid, Sumpter, & Beekman, 2011) and decreasing travel times and lowering the energetic costs of walking that balance competing design objectives (Cabanes, van (Fewell, 1988; Howard, 2001; Rockwood & Hubbell, 1987). In a Wilgenburg, Beekman, & Latty, 2014; Cook, Franks, & Robinson, study on the western harvester ant, Pogonomyrmex occidentalis, 2014). Some ant species also physically alter the substrate by researchers found that despite travelling a longer overall distance clearing the trail system of any vegetation. For example, leaf- on cleared paths, foragers were able to move faster and return more cutting ants such as Atta colombica are well known for clearing resources to the colony (Fewell, 1988). Similarly, A. colombica for- vegetation and obstacles to form trunk trails which can last for agers moved more quickly on cleared trails than on uncleared trails months or even years, providing colonies with transport routes that (Bochynek, Meyer, & Burd, 2017). act as highways throughout their foraging territory (Howard, 2001; Meat ants are capable of making trade-offs when selecting be- Rockwood & Hubbell, 1987). Trail clearing has been described in tween paths that vary in their level of obstruction and length. When ants from several genera including; Atta, Formica, Lasius and offered a choice between a short bridge covered in artificial grass or a Pogonomyrmex (see; Holldobler€ & Wilson, 1990), Messor (see; smooth bridge that was 50% longer than the short path, meat ant colonies always chose the short path (Luo, Reid, Makinson, Beekman, & Latty, 2018). The colonies rerouted trafficaroundsemipermeable obstacles made of turf grass when the obstacles were short but * Correspondence: C.R. Reid, Department of Biological Sciences, Macquarie tended to walk through the grass when the obstruction was long, University, Sydney, NSW 2109, Australia. E-mail address: [email protected] (C. R. Reid). presumably to avoid a long detour. 1 Both authors contributed equally to this work. https://doi.org/10.1016/j.anbehav.2019.02.006 0003-3472/© 2019 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. 98 E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111

Although trail clearing involves the cutting and removal of hundreds or thousands of items, the energetic costs to the colony are thought to be minimal compared to the energetic benefits obtained by walking on an unobstructed path (Howard, 2001). However, Bochynek et al. (2017) suggested that, in addition to the energetic cost of physically cutting and removing debris, ants also incur an opportunity cost because ants involved in trail construction and maintenance are not available for other activities such as foraging, defence and nest maintenance. Trail-laying ants are sometimes observed transporting food along uncleared trails (E. J. T. Middleton, T. Latty & C. R. Reid, personal observation; Bochynek et al., 2017), suggesting that trail clearing is not always profitable. A reasonable explanation for the existence of uncleared trails is that they represent sections of trail where the costs of trail construction/ maintenance exceed the benefits obtained from clearing, or alternatively, not enough time has passed to see the development of a cleared trail. Previous studies on the energetics of trail clearing do not take variability in the difficulty presented by some kinds of obstructions into account. Obstacles can vary both in toughness and in abundance and these characteristics might influence the decision to invest in trail clearing. Here we determined whether clearing investment was influenced by (1) the number of obstacles and/or (2) the difficulty of obstacle removal. In addition to studying the impact of obstacle characteristics on clearing rates, we also investigated the efficiency and organization of trail formation by examining the spatial pattern of trail clearing. Trail construction rules could range from a random process in which ants build their trails by randomly removing obstacles between the nest and the target, to a highly coordinated model in which ants sequentially remove obstacles along a direct line be- Figure 1. Meat ant colony (centre) displaying cleared trails radiating from the main tween resources. Random construction processes have the benefit nest mound. Reproduced with permission: Nathan Brown. of not requiring extensive coordination between clearer ants; each ant can simply remove whichever obstacle it comes into contact to remain active for several days despite heavy exploitation. The with. However, a random process is less efficient as it can lead to feeder was placed in a rectangular box made from polypropylene the removal of obstacles that do not lie directly along the emerging flute board (Corflute), which also encompassed the obstacle path, leading to wasted effort as well as a longer time to develop a runway (Fig. 2a). The runway end of the box was placed in direct connected path. contact with the nest mound edge of the focal colony. Inside the runway, we inserted a Corflute floor in which was METHODS embedded rows of artificial ‘grass’ in the form of vertical laser-cut green paper or card ‘grass blades’ (Fig. 2b, c). These rows sat in Study Species grooves cut into the Corflute and were held in place by the slight compression of the entire runway as it was placed into the arena. The polydomous Australian meat ant is found across Australia, Ants were able to move between or over the artificial blades of in colonies containing hundreds of thousands of monomorphic grass, but with some difficulty (E. J. T. Middleton, T. Latty & C. R. workers (Greaves & Hughes,1974; Shattuck, 2000). They maintain a Reid, personal observation). Each blade was spatially offset from network of distinct trails cleared of vegetation between nest en- the corresponding blade in the adjacent columns, such that ants trances and stable food sources, such as homopteran-infested trees travelling between blades immediately faced another blade (see (Greaves & Hughes, 1974; Van Wilgenburg & Elgar, 2007; Fig. 1). Fig. 2c). We covered the runway with a sheet of clear acetate to Meat ants also utilize trail pheromones while foraging (Van provide visual access and shelter from rain. Wilgenburg & Elgar, 2007). We tested the impact of obstacle characteristics by altering the abundance and toughness of the artificial grass in the runways. Our Study Site treatments included four combinations of two variables that dictate the difficulty of clearing a trail: the number of columns of obstacles We studied meat ant colonies on the Hawkesbury campus of and the difficulty in clearing (cutting) each obstacle. To alter Western Sydney University in Richmond, New South Wales, obstacle abundance, we used treatments with either five or 15 Australia, from October 2015 to February 2016. The field site was columns of artificial grass. To alter obstacle toughness, we used located within a large remnant patch of Cumberland plains either standard printer paper (80 g/m2, easy, denoted ‘P’) or card woodland, dominated primarily by open stands of Eucalyptus trees. (210 g/m2, difficult, denoted ‘C’) when making the rows of grass obstacles. C is therefore 2.6 tougher than P. We thus had four Experimental Set-up treatments; five columns of paper (5P), 15 columns of paper (15P), five columns of card (5C) and 15 columns of card (15C). We filmed To entice meat ants to build trails, we gave colonies access to a ants travelling across the Corflute set-up with no obstacles (N ¼ 15) gravity-fed sugar-water feeder containing a 0.5 M sucrose solution. and with the 15 columns of paper obstacles (N ¼ 15). We performed The feeder had a large capacity (700 ml) which allowed the feeder a Welch two-sample t test in R v3.3.0 (R Core Team, 2018)to E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111 99

(a)

300 mm (b) Towards nest Towards feeder 1 2 3 4 5 6 7 8 9 10 2 mm 11 100 12 3 mm mm 13 14 15 mm 15 16 17 18 19 20 ABCDEFGHIJKLMNO (c)

15 15 mm mm

2 mm 3 mm 100 mm

Figure 2. (a) Schematic of experimental apparatus. The runway (foreground) contains the rows of obstacles, with the feeder located in the rear chamber. Some opaque walls have been made transparent for illustrative purposes. The runway and feeder chamber were roofed with sheets of clear acetate. (b) Overhead view of the spatial arrangement of laser-cut paper ‘grass’ blades in the runway. (c) Frontal view of the spatial arrangement of obstacles. 100 E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111 determine whether the time taken to cross was significantly First, the artificial grass grid was converted to a network grid, with different between the clear and the obstacle-rich treatments. each grass blade (denoted by the 2 mm marker in Fig. 2c) corre- We allowed ants to feed from and recruit to the feeder for 2 sponding to an inaccessible node and each opening between two days, at which point we replenished the sugar-water. At this time, grass blades (denoted by the 3 mm marker in Fig. 2c) correspond- we recorded the location and number of blades that had been ing to an accessible node. In the natural environment, grass blades cleared by the ants, where ‘cleared’ is defined as being completely are not entirely inaccessible (ants were occasionally observed removed. We repeated this procedure a further 2 days later, at travelling over them); however, travel over a grass blade incurs a which point we ceased the experiment. cost that we represented as an inaccessible node in the artificial We split the treatments evenly between 20 colonies during four grid. All nodes on the network grid were connected to their im- experimental periods (five colonies per treatment per period). Each mediate neighbours (whether accessible or not) to their right and colony received each treatment once, and treatments were ran- left, and front and back, respective to the A to O direction (or E for domized across colonies. All colonies were given 9 days rest before the low obstacle abundance treatment). We then used the un- retesting to limit satiation or learning effects. This protocol also weighted breadth-first search algorithm (West, 2001) imple- controlled for weather and seasonal effects, as any weather changes mented in the R igraph package (Csardi & Nepusz, 2006)to were experienced equally across all treatments. calculate the length of the shortest paths through accessible nodes between each location on the starting column (column A, closest to Spatial Pattern of Trail Clearing nest) and the closest location on the final column (column E or O, closest to feeder). The resulting ‘percolation times’ (‘time’ and ‘path The optimal clearing pattern would result in a cleared path that length’ are used interchangeably here), for each experimental forms a straight line between the nest and the food source. To replicate were then normalized between the theoretical minimum determine whether meat ants produce such a clearing pattern, we percolation time if all grass blades were cleared, and therefore all counted the cumulative number of clearing events for each grass nodes accessible (10 nodes for the five paper and five card set-ups; blade location over the 4 days and visualized the data as a heat map 30 nodes for the 15 paper and 15 card set-ups), and the theoretical of clearing likelihood. We examined the pattern of clearing in all maximum time if no obstacles were cleared. rows and all columns as separate graphs, with the data presented as To determine whether the ants' behaviour represented a local area (LOESS) regressions and box plots (Appendix Figs. A1 and clearing strategy to improve travel along the trail, we compared A2). The locations of clearing events by Days 2 and 4 for all repli- the clearing pattern in each replicate to a simulated pattern that cates in each treatment are shown in Appendix Figs. A3eA6. All removed the same number of cleared grass obstacles in a random graphs were plotted using R v3.3.0 and the ggplot2 package fashion. For each replicate at Day 2 and at Day 4, we simulated (Wickham, 2009). 10 000 grids containing the same number of noncleared grass blades, placed at random throughout the grid. We then calculated Impact of Toughness and Abundance on Trail Clearing the normalized mean percolation time for each of these random grids using the same method as for the experimental replicates. We performed a generalized linear mixed-effects analysis Finally, we computed the difference between these normalized (Bates, Maechler, & Bolker, 2012) of the relationship between the mean percolation times and the corresponding experimental obstacle abundance and toughness and the amount of clearing normalized mean percolation times (10 000 differences per repli- observed for each day using the lme4 package in R 3.5.1 (Bates, cate per day). We then determined whether the cutting pattern of Maechler, Bolker, & Walker, 2015; R Core Team, 2018). The a particular replicate on a particular day was significantly different amount of clearing was quantified as either the number of blades from random by simply checking that a zero difference did not fall cleared (in which case we used the Poisson family with a log link) between the 2.5th and 97.5th percentile of the corresponding or as the proportion of blades cleared (in which case we used the distribution of differences. The presence of a better-than-random binomial family with a logit link). Using both analyses allowed us clearing pattern would support a clearing strategy in the ants' to compare the relative amount of clearing across cases (propor- behaviour. tion) while also examining the individual amount of effort We ran a generalized linear mixed-effects analysis of the rela- involved in clearing in each treatment (number). In all cases, we tionship between the obstacle abundance and toughness and the treated Day 2 and Day 4 of the experiment separately. For the normalized mean percolation time for each day as created by our number of blades cleared, toughness was used as fixed effects percolation analysis. This analysis used the same method as in the (with interaction term) and colony number was included as a previous section (but using the gaussian family). random effect. The two obstacle abundance treatments were treated separately because the number of blades cleared is Ethical Note necessarily affected by the total number of blades available for clearing in the first place. In the case of the proportion of blades No certificate or licensing numbers or ethics approval were cleared, obstacle abundance and toughness were used as fixed required, as study of this species does not require any government effects (with interaction term) and colony number was included as or institutional approval within Australia. The experimental design a random effect. The experimental period was not included in the allowed for minimal disruption to ant behaviour: the apparatus model since all treatments were tested simultaneously in each was laid out close to a nest but not directly on a nest, and the in- period to control for random effects such as weather. We report dividuals were left to feed and interact with the apparatus without the incidence rate ratio (IRR), which is defined as the number of manipulation. During data collection, we merely observed the events in one category divided by the number of events in the apparatus and recorded our findings, disrupting the feeding ants other category (Sedgwick, 2010). only when removing the apparatus at the end of the experiment. Individuals that may still have been feeding on the food source or Efficiency of Trail Clearing within the runway were gently brushed off close to the nest. Our experimental apparatus was designed to be completely removable We developed a percolation analysis to determine whether the and reusable at another location, thus leaving no permanent impact ants' clearing behaviour improved the ease of travel along the trail. on the site. E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111 101

RESULTS To cross a clear path, foragers took an average ±SE of 1.79 ± 0.25 s (N ¼ 15), as opposed to 6.70 ± 0.76 s (N ¼ 15) to cross Individual Clearing Behaviour 15 rows of paper obstacles (t ¼ 6.15, P < 0.001). This demonstrates a signiﬁcant increase in the amount of time it took a forager to cross Meat ants actively removed obstacles by using their mandibles our set-up when obstacles were present. to cut the obstacles off at the base. They grasped the base of the obstacle with their mandibles and then typically proceeded to tug Spatial Pattern of Trail Clearing at it for a short period of time. Preliminary observations from a related experiment which used the same obstacles found that, on Although not a strong pattern, Fig. 3 shows that obstacles average, ants performed obstacle cutting for an average of 5.5 s around rows 8e12 (correlating with the entrance and exit of the before releasing the obstacle and wandering away. We rarely set-up) had the highest cumulative clearing rate, and high clearing observed a single ant start and complete obstacle removal. rates occurred in a semicircle extending from these points, with

Nest Feeder Nest Feeder (a) (b) 1 1 2 2 3 3 4 4 5 5 6 6 Cumulative 7 7 number of 8 8 clearing 9 9 events 20 10 10 11 11 15 12 12 10 13 13 5 14 14 15 15 0 16 16 17 17 18 18 19 19 20 20 A BCDE ABCDEFGHIJKLMNO (c) (d) 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10

Row11 11 Row 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 ABCDE ABCDE FGHI J KLMNO Column

Figure 3. Heat maps showing the cumulative number of clearing events at each blade location across all replicates for each treatment: (a) 5P (five rows of paper grass), (b) 15P (15 rows of paper grass), (c) 5C (five rows of card grass) and (d) 15C (15 rows of card grass) (Day 4, N ¼ 20 for all treatments). Ants entered and exited the runway through openings between rows 8 and 13 on either side. 102 E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111 zeniths inwardly aligned. This pattern is also clear when examining Proportion of obstacles cleared per replicate the spatial clearing pattern averaged over each row and column For both Day 2 and Day 4, there was no interaction effect be- (Appendix Figs. A1 and A2) and when viewing the raw data tween obstacle abundance and obstacle toughness when (Appendix Figs. A3eA6) and suggests that ants focus their clearing comparing the models with and without interaction (Day 2: effort on the areas closest to the nest and the resource. c2 ¼ 1.446, P ¼ 0.229; Day 4: c2 ¼ 0.182, P ¼ 0.893). For Day 2, decreasing both obstacle abundance (IRR ¼ 6.27, CI ¼ 1.28e30.78, Impact of Toughness and Abundance on Trail Clearing P ¼ 0.024, conditional R2 ¼ 0.611) and toughness (IRR ¼ 22.30, CI ¼ 3.12e159.43, P ¼ 0.002, conditional R2 ¼ 0.611) resulted in a Number of obstacles cleared per replicate significantly increased proportion of obstacles cleared per replicate For Day 2, there was a significant effect of obstacle toughness on (Fig. 4b). The same relationship held for Day 4 (abundance: the number of blades cleared. Softer obstacles were much more IRR ¼ 6.91, CI ¼ 1.70e28.07, P ¼ 0.007, conditional R2 ¼ 0.594; likely to be cleared than tougher obstacles for both the low abun- toughness: IRR ¼ 16.54, CI ¼ 3.37e81.26, P ¼ 0.001, conditional dance (incidence rate ratio, IRR ¼ 2.64, confidence interval, R2 ¼ 0.594; Fig. 4b). CI ¼ 2.35e2.96, P < 0.001, conditional R2 ¼ 0.975; Fig. 4a) and high By Day 4, proportionately fewer obstacles were cleared on high abundance (IRR ¼ 10.90, CI ¼ 9.44e12.58, P < 0.001, conditional abundance treatments than on low abundance treatments, and R2 ¼ 0.992; Fig. 4a) treatments. Similar results were obtained for more obstacles were removed in the low toughness treatment Day 4: softer obstacles were much more likely to be cleared than than the high toughness treatment. This suggests that ants tougher obstacles for both the low abundance (IRR ¼ 1.98, perform more clearing when obstacle abundance and toughness CI ¼ 1.82e2.16, P < 0.001, conditional R2 ¼ 0.976; Fig. 4a) and high are low. The similarity on Day 2 in the median number of clearing abundance (IRR ¼ 3.06, CI ¼ 2.86e3.28, P < 0.001, conditional events within toughness treatments (Fig. 4a) suggests that the R2 ¼ 0.994; Fig. 4a) treatments. colonies invested the same amount of effort in clearing each

300 (a) Day 2 4

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100 Number of blades cleared

0 5P 15P 5C 15C

1 (b)

0.75

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0.25 Proportion of blades cleared

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Figure 4. Box plots representing the distributions of (a) the number of grass blades cleared and (b) the proportion of grass blades cleared, as a function of treatment (5P, 15P, 5C and 15C) and time (Days 2 and 4). Horizontal dashed lines show the maximum possible number of grass blades in each treatment. The box plots show the median and 25th and 75th percentiles; the whiskers indicate the values within 1.5 times the interquartile range and the circles are outliers. E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111 103 treatment, but tougher obstacles took longer to remove, resulting normalized mean percolation times obtained under the assump- in fewer blades cleared. tion of random cutting. For both Day 2 (Fig. 6a) and Day 4 (Fig. 6b), the number of replicates that performed better than random cut- Efficiency of Trail Clearing ting (negative differences) increased with the proportion of blades cleared, before returning towards zero for proportions >0.5. Indeed, All treatments showed a decrease in percolation time between for a large number of cleared blades, even a random cutting strat- Days 2 and 4 (Fig. 5), indicating that clearing continued over the egy eventually cleared up more direct paths through the runway. course of our experiment, and resulted in a quicker passage be- However, when the proportion of blades cleared was lower than tween the nest and food source. The GLMM analysis for Day 2 0.5, the ants' clearing strategy performed generally better than revealed that there was no interaction effect between obstacle random (ca. 7.5% better on average). Further, when comparing abundance and obstacle toughness on the normalized mean patterns across treatments, it is apparent that the pattern of cutting percolation time when comparing the models with and without was initially less random for high abundance treatments (Fig. 6e, f) interaction (c2 ¼ 0.372, P ¼ 0.542; Fig. 5). Decreasing both obstacle before returning towards zero for proportions >0.25, as compared abundance (B ¼0.19, CI ¼0.31 to 0.07, P ¼ 0.002, conditional with low abundance treatments (Fig. 6c, d). Similarly, more repli- R2 ¼ 0.324; Fig. 5) and toughness (B ¼0.32, CI ¼0.44 to 0.20, P cates had a better-than-random clearing pattern in the high < 0.001, conditional R2 ¼ 0.324; Fig. 5) led to significantly shorter toughness treatments (Fig. 6d, f) than in the low toughness treat- percolation times in the cleared trials. On Day 4 again neither ments (Fig. 6c, e). obstacle abundance nor obstacle toughness had a significant interaction effect on the normalized mean percolation time when DISCUSSION comparing the models with and without interaction (c2 ¼ 0.04, P ¼ 0.841; see Fig. 5). Decreasing both obstacle abundance Meat ant colonies in our study readily cleared away artificial (B ¼0.21, CI ¼0.34 to 0.07, P ¼ 0.002, conditional R2 ¼ 0.284; obstructions placed between the nest and a rewarding feeder; Fig. 5) and toughness (B ¼0.33, CI ¼0.46 to 0.19, P < 0.001, indeed, we observed evidence of some trail clearing in 90% of our conditional R2 ¼ 0.284; Fig. 5) led to significantly shorter percola- trials. On the second day of clearing, obstacle toughness influenced tion times in the cleared trials. the number of blades cleared, but obstacle abundance did not. This Fig. 6 summarizes the comparison between the normalized suggests that meat ants do not increase relative investment to mean percolation times obtained for each replicate each day and clearing when obstructions are abundant and instead allocate a set

Day 2 4

0.75

0.5 Normalized mean percolation time Normalized mean percolation

0.25

5P 15P 5C 15C Treatment

Figure 5. Normalized mean percolation time (in number of traversed grid nodes) as a function of treatment (5P, 15P, 5C and 15C) and observation day. All percolation times fell between the minimal and maximal percolation times for each treatment, normalized to a range between 0 and 1, respectively. The box plots show the median and 25th and 75th percentiles; the whiskers indicate the values within 1.5 times the interquartile range and the circles are outliers. 104 E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111

(a) (c) (e) 0.1 0.1 0.1

0 0 0

–0.1 –0.1 –0.1

–0.2 –0.2 –0.2 0 0.25 0.5 0.75 1 0 0.25 0.5 0.75 1 0 0.25 0.5 0.75 1

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Difference from random 0 0 0

–0.1 –0.1 –0.1

–0.2 –0.2 –0.2 0 0.25 0.5 0.75 1 0 0.25 0.5 0.75 1 0 0.25 0.5 0.75 1 Proportion of blades cleared

Figure 6. Difference between the normalized mean percolation time obtained for each replicate and normalized mean percolation times obtained under the assumption of random cutting (10 000 random samples per replicate) for each day and treatment: (a) Day 2, (b) Day 4, (c) 5P, (d) 5C, (e) 15P and (f) 15C. Each dot represents the average difference between an experimental measurement and 10 000 random samples with the same number of blades cleared. Each vertical bar represents the [2.5; 97.5] percentile interval for each mean difference. If a bar crosses the zero line (denoted in purple), then this replicate is considered nonsignificantly different from the corresponding random distribution. If it does not, then it is considered significantly better (green) or worse (orange) than random clearing. The dashed line and the upper and lower limits of the grey polygon are LOESS fits to the mean difference, 97.5th percentile and 2.5th percentile, respectively.

amount of effort to the task of obstacle clearing. On the fourth day, similar process is behind the corpse piles of some ant species, the rate of obstruction removal was influenced by both the where distinct piles form because ants are more likely to deposit toughness of the obstructions and their abundance; however, the their dead near other dead ants (Deneubourg, et al., 1991; effect of abundance is likely to be an experimental artefact driven Theraulaz et al., 2003). This type of positive feedback is com- by the fact that by Day 4, the low abundance treatment had been mon to many aspects of colony life (e.g. lane formation in army almost completely cleared of obstructions and thus had reached the ants, Couzin & Franks, 2003; nest relocation quorum sensing in maximum possible amount of clearing. Although meat ants did not ants, Pratt, 2005). clear an optimal unbroken path, their clearing activities resulted in Physical interactions between nestmates may also influence a pattern that performed better under percolation analysis than did clearing behaviour, as they do for a number of collective behaviours random clearing. Meat ants generally focused clearing in the areas (Gordon & Mehdiabadi, 1999; Greene & Gordon, 2007; Pinter- nearest to the resource and the nest. They performed better than Wollman, et al., 2013). An increase in physical interactions be- random by ca. 7.5% on average in the initial removal of obstacles (i.e. tween workers engaged in foraging could indicate a blockage or until 50% of the obstacles were removed, on both Days 2 and 4); constriction of the foraging trail, to which an efficient response they also performed better than random when clearing the first 25% would be to upregulate clearing behaviour to return trafficto of obstacles from high abundance treatments and when clearing maximum flow (Gordon & Mehdiabadi, 1999; Greene & Gordon, tougher obstacles. This suggests that there is an initial investment 2007; Pinter-Wollman et al., 2013). of effort in clearing when the abundance and toughness of obsta- Our results have implications for the energetics of trail cles are high. After 50% of obstacles are removed, the pattern of clearing and route finding in meat ants. While several models clearing no longer produces travel routes that are better than have attempted to calculate the energetics of trail clearing random obstacle removal. (Bochynek, 2017; Howard, 2001), all have assumed that obstacles Our percolation analysis shows that meat ant clearing behav- are equally difficult to remove. In our experiment, ants were iour was significantly better than random clearing at improving capable of removing the tougher card blades, but took longer to ease of travel. The mechanisms underlying the pattern of trail do so. Difficult-to-clear objects would extend the amount of time clearing are unknown. Meat ants use pheromones for nestmate it took for ants to clear a path. An increase in trail-clearing time recognition (Thomas, Parry, Allan, & Elgar, 1999)andasthepri- would translate into delayed benefits as it would take longer for mary navigational tool of naïve recruited foragers (Card, the ants' investment in trail clearing to pay off. The nonrandom McDermott, & Narendra, 2016). In some ants and termites, pher- clearing behaviour we observed would help mitigate this issue omones drive stigmergic building processes resulting in struc- because the ants benefit from decreased travel times even before tures such as nest mounds and tunnels (Theraulaz, Gautrais, a path is fully cleared. Camazine, & Deneubourg, 2003; Khuong, et al., 2016). Alterna- Meat ant colonies varied in the rate at which they removed tively, nonrandom clearing could emerge if ants were simply obstacles (Figs. A3eA6) with some colonies removing all obstacles more likely to cut blades of grass adjacent to those that have within 2 days while others removed few or no obstacles after 4 already been cut, or if workers encountering nestmates engaged days. Some colonies were consistently low removers; colony 1, for in clearing were more likely to begin clearing themselves. A example had one of the lowest clearing rates, clearing a mean 14.5% E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111 105

(±8) of obstacles across all trials. In contrast, colony 17 consistently in Science grant and an Australian Research Council Discovery had one of the highest clearing rates, removing a mean 75% (±15). grant (DP140103643) (both to T.L.). Colonies differed in their traffic levels; however, previous research found no correlation between traffic level and clearing rate (Luo References et al., 2018). While we did not explicitly measure environmental factors such as temperature and sun exposure, we often observed Acosta, F. J., Lopez, F., & Serrano, J. M. (1993). Branching angles of ant trunk trails as e colonies on the same day, in similar environmental conditions, with an optimization cue. Journal of Theoretical Biology, 160(3), 297 310. https:// doi.org/10.1006/jtbi.1993.1020. vastly different clearing rates. Thus, we suspect that factors Acosta, F. J., Lopez, F., & Serrano, J. M. (1995). Dispersed versus central-place intrinsic to the colony (population size, genetic background) may foraging: Intra-and intercolonial competition in the strategy of trunk trail e be important drivers of clearing rate. Understanding the mecha- arrangement of a harvester ant. American Naturalist, 145(3), 389 411. Bates, D., Maechler, M., & Bolker, B. (2012). lme4: Linear mixed-effects models using S4 nism underlying variation in clearing rate will be an interesting classes. R package version 0.999375-42 http://cran.r-project.org/package¼lme4. avenue for further study. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects Since trail clearing probably represents an energetic investment, models using lme4. Journal of Statistical Software, 67(1), 1e48. https://doi.org/ 10.18637/jss.v067.i01. we expect it to occur only when food sources are persistent. In our Bochynek, T., Meyer, B., & Burd, M. (2017). Energetics of trail clearing in the leaf- study, however, trail clearing was underway after only 2 days, with cutter ant Atta. Behavioral Ecology and Sociobiology, 71(1), 14. https://doi.org/ some colonies initiating clearing on the first day. Meat ants acquire 10.1007/s00265-016-2237-5. Cabanes, G., van Wilgenburg, E., Beekman, M., & Latty, T. (2014). Ants build trans- carbohydrates primarily from honeydew secreted by sap-sucking portation networks that optimize cost and efficiency at the expense of hemipterans. While there are no specific data on the stability of robustness. Behavioral Ecology, 26(1). https://doi.org/10.1093/beheco/aru175. these resources, it is generally assumed that sap-sucking hemip- Card, A., McDermott, C., & Narendra, A. (2016). Multiple orientation cues in an Australian trunk-trail-forming ant, Iridomyrmex purpureus. Australian Journal of terans in food trees represent a stable food source (Van Wilgenburg Zoology, 64(3), 227e232. & Elgar, 2007). It may be that meat ants simply employ a decision Cook, Z., Franks, D., & Robinson, E. H. (2014). Efficiency and robustness of ant colony rule whereby they always clear trails to sugary resources as, in their transportation networks. Behavioral Ecology and Sociobiology, 68(3), 509e517. natural environment, these will generally represent hemipteran https://doi.org/10.1007/s00265-013-1665-8. Couzin, I. D., & Franks, N. R. (2003). Self-organized lane formation and optimized aggregations. Alternatively, meat ants might be more likely to clear traffic flow in army ants. Proceedings of the Royal Society of London. Series B: trails leading to high-value resources such as our feeders. Future Biological Sciences, 270(1511), 139e146. https://doi.org/10.1098/rspb.2002.2210. Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research should focus on understanding how clearing behaviour is e fl research. InterJournal, Complex Systems, 1695(5), 1 9. in uenced by both food quality and stability. Deneubourg, J.-L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., & Chretien, L. Differential investment during trail clearing could be influenced (1991). The dynamics of collective sorting robot-like ants and ant-like robots. In by a range of factors such as colony size, individual preferences, J. A. Meyer, & S. W. Wilson (Eds.), From animals to animats. Proceedings of the first international conference on simulation of adaptive behaviour (pp. 356e363). colony level hunger and temperatures to name a few. Our study Cambridge, MA: MIT Press. provides a tool for the direct comparison of clearing behaviours in Fewell, J. H. (1988). Energetic and time costs of foraging in harvester ants, Pogo- natural conditions that could help to shed light on questions nomyrmex occidentalis. Behavioral Ecology and Sociobiology, 22(6), 401e408. https://doi.org/10.1007/bf00294977. regarding trail-clearing behaviours and energy investment at the Gamboa, G. J. (1975). Foraging and leaf-cutting of the desert gardening ant Acro- colony level. myrmex versicolor versicolor (Pergande) (Hymenoptera: Formicidae). Oecologia, 20(1), 103e110. Gordon, D. M., & Mehdiabadi, N. J. (1999). Encounter rate and task allocation in Author contributions harvester ants. Behavioral Ecology and Sociobiology, 45(5), 370e377. https:// doi.org/10.1007/s002650050573. T.L., C.R. and E.M. conceived of and designed the study; E.M. and Greaves, T., & Hughes, R. (1974). The population biology of the meat ant. Australian fi Journal of Entomology, 13(4), 329e351. C.R. collected the eld data and processed the data for analyses; Greene, M. J., & Gordon, D. M. (2007). Interaction rate informs harvester ant task E.M., T.L. and C.R. carried out statistical analyses for the absolute decisions. Behavioral Ecology, 18(2), 451e455. https://doi.org/10.1093/beheco/ and proportional clearing data; S.G. designed and carried out the arl105. € percolation analyses; E.M. and C.R. drafted the initial manuscript Holldobler, B., & Wilson, E. O. (1990). The ants. Cambridge, MA: Harvard University Press. with subsequent input from all authors; all authors gave their final Howard, J. J. (2001). Costs of trail construction and maintenance in the leaf-cutting approval for publication. ant Atta columbica. Behavioral Ecology and Sociobiology, 49(5), 348e356. https:// doi.org/10.1007/s002650000314. Khuong, A., Gautrais, J., Perna, A., Sbaï, C., Combe, M., Kuntz, P., et al. (2016). Stig- Conflict of interest mergic construction and topochemical information shape ant nest architecture. Proceedings of the National Academy of Sciences, 113(5), 1303e1308. We have no competing interests. Latty, T., Ramsch, K., Ito, K., Nakagaki, T., Sumpter, D. J., Middendorf, M., et al. (2011). Structure and formation of ant transportation networks. Journal of the Royal Society Interface, 8(62), 1298e1306. Data availability Luo, D., Reid, C. R., Makinson, J. C., Beekman, M., & Latty, T. (2018). Route selection but not trail clearing are influenced by detour length in the Australian meat ants. Insectes Sociaux, 66(47), 1e10. https://doi.org/10.1007/s00040-018-0658-z. The data and code written for calculating the results are avail- Marlin, J. (1971). The mating, nesting and ant enemies of Polyergus lucidus Mayr able at https://github.com/sjmgarnier/trailClearing. (Hymenoptera: Formicidae). American Midland Naturalist, 86,181e189. Pinter-Wollman, N., Bala, A., Merrell, A., Queirolo, J., Stumpe, M. C., Holmes, S., et al. (2013). Harvester ants use interactions to regulate forager activation and Acknowledgments availability. Animal Behaviour, 86(1), 197e207. https://doi.org/10.1016/j.anbehav. 2013.05.012. € We acknowledge the field and experimental construction Plowes, N. J., Johnson, R. A., & Holldobler, B. (2013). Foraging behavior in the ant genus Messor (Hymenoptera: Formicidae: Myrmicinae). Myrmecological News, assistance of Jessica Boyes, Rebecca Hartstein, Caitlyn Drayton- 18,33e49. Taylor and Alexa Caputo. We also thank Michael Duncan for Pratt, S. C. (2005). Quorum sensing by encounter rates in the ant Temnothorax invaluable knowledge and assistance in the field and Nathan Brown albipennis. Behavioral Ecology, 16(2), 488e496. https://doi.org/10.1093/beheco/ ari020. for permission to use his photo of a beautifully cleared meat ant R Core Team. (2018). R: A language and environment for statistical computing. Vienna, nest. Funding for this project came from the Branco Weiss Society Austria: R Foundation for Statistical Computing. https://www.R-project.org/. 106 E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111

Reid, C. R., Sumpter, D. J., & Beekman, M. (2011). Optimisation in a natural system: Van Wilgenburg, E., & Elgar, M. A. (2007). Colony structure and spatial distribution Argentine ants solve the Towers of Hanoi. Journal of Experimental Biology, of food resources in the polydomous meat ant Iridomyrmex purpureus. Insectes 214(1), 50e58. Sociaux, 54(1), 5e10. Rockwood, L. L., & Hubbell, S. P. (1987). Host-plant selection, diet diversity, and West, D. B. (2001). In Introduction to graph theory (Vol. 2). Upper Saddle River, NJ: optimal foraging in a tropical leafcutting ant. Oecologia, 74(1), 55e61. https:// Prentice Hall. doi.org/10.1007/BF00377345. Wetterer, J. K. (1995). Forager size and ecology of Acromyrmex coronatus and other Sedgwick, P. (2010). Incidence rate ratio. British Medical Journal, 341. c4804. leaf-cutting ants in Costa Rica. Oecologia, 104(4), 409e415. Shattuck, S. (2000). Australian ants: Their biology and identiﬁcation. Clayton, Wickham, H. (2009). ggplot2: Elegant graphics for data analysis. New York, NY: Australia: CSIRO. Springer-Verlag. Theraulaz, G., Gautrais, J., Camazine, S., & Deneubourg, J.-L. (2003). The formation of spatial patterns in social insects: From simple behaviours to complex struc- APPENDIX tures. Philosophical Transactions of the Royal Society of London. Series A: Math- ematical, Physical and Engineering Sciences, 361(1807), 1263e1282. https:// doi.org/10.1098/rsta.2003.1198. Thomas, M. L., Parry, L. J., Allan, R. A., & Elgar, M. A. (1999). Geographic afﬁnity, cuticular hydrocarbons and colony recognition in the Australian meat ant Iri- domyrmex purpureus. Naturwissenschaften, 86(2), 87e92. https://doi.org/ 10.1007/s001140050578.

20 (a)20 (b) 19 19 Day 2 4 18 18 17 17 16 16 15 15 14 14 13 13 12 12 11 11 10 10 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 1 1 0 0 ABCDE ABCDEFGHI J KLMNO

20 (c)20 (d) 19 19 18 18 17 17 16 16 15 15

Cumulative number of clearing events 14 14 13 13 12 12 11 11 10 10 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 1 1 0 0 ABCDE ABCDE FGHI J KLMNO Column

Figure A1. Plots showing the cumulative clearing events in each column for all replicates (summed across rows) for each treatment: (a) 5P, (b) 15P, (c) 5C and (d) 15C (N ¼ 20 for all treatments). Circles are individual replicate data, jittered to limit overlap. Solid lines are LOESS curves with 95% conﬁdence intervals represented by a semitransparent envelope. These curves show the trend in clearing when looking across columns of obstacles. Left is closest to the nest, right is closest to the feeder. The box plots show the median and 25th and 75th percentiles; the whiskers indicate the values within 1.5 times the interquartile range. E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111 107

1 (a)1 (b) Day 2 4 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20

01234567891011121314151617181920 0 1 2 3 4 5 6 7 8 9 1011121314151617181920 Row 1 (c) 1 (d) 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 01234567891011121314151617181920 0 1 2 3 4 5 6 7 8 9 1011121314151617181920 Cumulative number of clearing events

Figure A2. Plots showing the cumulative clearing events in each row for all replicates (summed across columns) for each treatment: (a) 5P, (b) 15P, (c) 5C and (d) 15C (N ¼ 20 for all treatments). Circles are individual replicate data, jittered to limit overlap. Solid lines are LOESS curves with 95% conﬁdence intervals represented by a semitransparent envelope. These curves show the trend in clearing when looking across rows of obstacles. Left is closest to the nest, right is closest to the feeder. The box plots show the median and 25th and 75th percentiles; the whiskers indicate the values within 1.5 times the interquartile range. 108 E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111

(a) 1 2345 1 5 10 15 20

6 78910 1 5 10 15 20

Row 11 12 13 14 15 1 5 10 15 20 16 17 18 19 20 1 5 10 15 20 ABCDE ABCDE ABCDE ABCDE ABCDE (b) 1 2345 1 5 10 15 20

6 78910 1 5 10 15 20

Row 11 12 13 14 15 1 5 10 15 20 16 17 18 19 20 1 5 10 15 20 ABCDE ABCDE ABCDE ABCDE ABCDE Column

Figure A3. Plots showing which grass blades were cleared (yellow) and uncleared (purple) for each colony (1e20) in the 5P treatment on (a) Day 2 and (b) Day 4. E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111 109

(a) 1 2345 1 5 10 15 20

6 78910 1 5 10 15 20

Row 11 12 13 14 15 1 5 10 15 20 16 17 18 19 20 1 5 10 15 20 ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO (b) 1 2345 1 5 10 15 20

6 78910 1 5 10 15 20

Row 11 12 13 14 15 1 5 10 15 20 16 17 18 19 20 1 5 10 15 20 ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO Column

Figure A4. Plots showing which grass blades were cleared (yellow) and uncleared (purple) for each colony (1e20) in the 15P treatment on (a) Day 2 and (b) Day 4. 110 E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111

(a) 1 2345 1 5 10 15 20

6 78910 1 5 10 15 20

Row 11 12 13 14 15 1 5 10 15 20 16 17 18 19 20 1 5 10 15 20 ABCDE ABCDE ABCDE ABCDE ABCDE (b) 1 2345 1 5 10 15 20

6 78910 1 5 10 15 20

Row 11 12 13 14 15 1 5 10 15 20 16 17 18 19 20 1 5 10 15 20 A BCDE A BCDE A BCDE A BCDE A BCDE Column

Figure A5. Plots showing which grass blades were cleared (yellow) and uncleared (purple) for each colony (1e20) in the 5C treatment on (a) Day 2 and (b) Day 4. E. J. T. Middleton et al. / Animal Behaviour 150 (2019) 97e111 111

(a) 1 2345 1 5 10 15 20

6 78910 1 5 10 15 20

Row 11 12 13 14 15 1 5 10 15 20 16 17 18 19 20 1 5 10 15 20 ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO (b) 1 2345 1 5 10 15 20

6 78910 1 5 10 15 20

Row 11 12 13 14 15 1 5 10 15 20 16 17 18 19 20 1 5 10 15 20 ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO ABCDEFGHIJKLMNO Column

Figure A6. Plots showing which grass blades were cleared (yellow) and uncleared (purple) for each colony (1e20) in the 15C treatment on (a) Day 2 and (b) Day 4.