Introduction Movement governs the way animals use habitats, interact with conspecifics, avoid predators and obtain food. When animals move, they make two simple but important decisions – where to move and how fast to do so. We know a lot about the factors affecting where animals move, thanks to decades of research by behavioural ecologists (e.g. foraging patch selection, retreat site selection) (e.g. Charnov 1976; Krebs & Kacelnik 2007), yet studies of speed choice by animals are remarkably rare (Wilson et al. 2015). Speed choice is not a trivial decision, as it determines the intensity and energetic cost of a given activity and affects its probability of success. In nature, animals seldom move at their maximum speeds – even during the rare fitness‐defining situations like predator escape and prey capture (Jayne & Ellis 1998; Irschick 2002; Irschick et al. 2005; Husak & Fox 2006). For instance, cheetahs almost never chase their prey at full speed because it is difficult to make sharp turns or maintain footing at high speeds (Wilson et al. 2013). Central to understanding why animals choose the speeds they do in nature is the recognition that high speeds have substantial costs (Clemente & Wilson 2015b; Wilson et al. 2015). Fast movement speeds are energetically costly (Hoyt & Taylor 1981; Steudel‐Numbers & Wall‐Scheffler 2009), constrain motor control and manoeuvrability (Alexander 1982; Wynn et al. 2015; Wilson et al. 2016) and can affect visibility and safety (Bednekoff & Lima 1998; Treves 1998). Thus, animals should select their movement speeds by balancing the benefits of high speeds against their associated costs (Wilson et al. 2015). Predator–prey interactions are an excellent starting point for examining the speed choice of animals, because the decisions made during pursuit or escape affect survival directly – no movement decision is more important (Clemente & Wilson 2015a). Previous studies of animal movement speeds in the context of predator–prey interactions have been largely descriptive (but see Wynn et al. 2015), and although they have nicely demonstrated that animals rarely use maximum speeds (Jayne & Ellis 1998; Irschick 2002; Irschick et al. 2005; Husak & Fox 2006), they have not shown us what intrinsic and extrinsic factors affect an animal's speed choice. This requires a quantitative framework that provides clear predictions and can be manipulated and tested in a variety of species and scenarios (Wilson et al. 2015). Wheatley et al. (2015) developed a simple model to predict optimal escape speeds for animals running along a straight beam. Beams or branches constrain movement by limiting the lateral extent of foot placement, and the increased curvature of beams with smaller diameters makes them even more challenging. Because faster running speeds are expected to compromise an animal's ability to accurately place their limbs (speed–accuracy trade‐off), Wheatley et al. (2015) assumed that: (i) the accuracy of foot placements decreases at faster running speeds, which manifests as a greater number of mistakes at high speeds, and (ii) mistakes (slips or stumbles) are costly, increasing the total time to traverse the beam and subsequently increasing the probability of capture and death. Wheatley et al.'s (2015) model thus predicts that animals should vary their running speeds according to the difficulty of the motor task (i.e. width of beam), choosing slower speeds on narrower beams. Evidence for the trade‐off between the speed an animal performs a movement and its level of control – otherwise referred to as the speed–accuracy trade‐off – primarily comes from studies of human performance (Fitts 1954; Wilson et al. 2014), but this has received virtually no attention for wild animals moving on ecologically realistic substrates (but see Sinervo & Losos 1991; Jayne, Lehmkuhl & Riley 2014). Although several previous studies have evaluated how movement speed changes when animals move on substrates of differing widths (Losos & Sinervo 1989; Losos & Irschick 1996; Lammers & Biknevicius 2004; Mattingly & Jayne 2004; Hyams, Jayne & Cameron 2012), only by quantifying the costs and frequency of motor mistakes can one begin to predict an animal's optimal speed choice. To the best of our knowledge, no quantitative data exist on how mistakes and their magnitude affect task success. In this study, we used northern quolls (Dasyurus hallucatus) to test the assumptions and predictions of Wheatley et al.'s (2015) model of optimal escape speeds when there is a trade‐off between speed and accuracy (i.e. motor control). Northern quolls are ideal for this study because they are semi‐arboreal, routinely using branches when escaping predators – including dingoes, birds of prey and snakes – or when attempting to capture insects and small mammals, reptiles and birds during foraging. First, we explored how a quoll's speed affected its probability of making mistakes when running along beams of differing width. Consistent with the trade‐off between speed and motor control, we expected that higher running speeds would increase the probability of making a mistake (slipping), particularly on narrower beams. Second, we quantified the costs of mistakes by measuring average movement speeds of northern quolls traversing the beam, testing the assumption that mistakes are costly because they slow animals down. Third, we tested whether individuals modulate their speed when running along beams, with slower running speeds expected on the narrower beams. Fourth, we determined whether quolls could improve their performance with repeated practice and thus attain higher running speeds over the 10 trials conducted at each beam width. Finally, we explored the morphological traits associated with better beam running. Based on the studies of lizard locomotion on arboreal substrates, we predicted that smaller quolls with shorter hindlimbs to have better beam running performance (Losos & Sinervo 1989; Irschick & Losos 1999).

Materials and methods Northern quolls were trapped on Groote Eylandt, Northern Territory, Australia, between July and August 2013 using Tomahawk original series cage traps (20 × 20 × 60 cm; Tomahawk ID‐103, Hazelhurst, Wisconsin, USA) baited with canned dog food. Traps were set overnight and checked early in the morning (no later than 07.30 h) to avoid quolls being subjected to warmer parts of the day in the traps. A total of 65 individual quolls (N = 65; 37 males, 28 females) were captured throughout the study period. Each quoll was taken to the Anindilyakwa Land and Sea Ranger Research Station for subsequent tests. A microchip was placed below the skin of each quoll between its shoulder blades (Trovan nano‐transponder ID‐100, Keysborough, Australia) for ease of identification during subsequent recaptures. Research methodologies were approved by the University of Queensland animal ethics committee (SBS/541/12/ANINDILYAKWA/MYBRAINSC) and were conducted under the Northern Territory Parks and Wildlife Commission (permit number: 47603). Body mass was measured for each individual using an electronic balance (±0·1 g; A & D Company Limited HL200i, Brisbane, Australia) and morphological variables were each measured three times using digital calipers (Whitworth, Brisbane, Australia, ±0·01 mm) (see Wynn et al. 2015). These body dimensions were head width (widest point of jaw), head length (from nuchal crest to tip of snout), body length (nuchal crest to base of tail), right and left forelimb length (radius–ulna), right and left hindlimb length (tibia–fibula), right and left hindfoot length (heel to claw base), tail width (maximum tail circumference) and tail length (base to tip of tail) (Fig. 1). Prior to locomotor tests, a 4 × 4 cm marker was fixed onto the fur of each individual between the shoulder blades to allow accurate tracking of movement during frame‐by‐frame analyses of video recordings. To minimize long‐term stress on the animals, all performance measures and tests were completed within 6 h of capture, after which the animals were released at the point of capture. Figure 1 Open in figure viewer PowerPoint Morphological measurements taken for each individual northern quoll, Dasyurus hallucatus, and the balance beam setup to assess motor control and balance. Morphological variables recorded include (a) head length (HL), body length (BL), right and left forearm length (FLL), right and left hindlimb length (HLL), right and left hindfoot length (FL), tail width (TW), tail length (TL) and (b) head width (HW). (c) The wooden dowel making up the beam track was angled at 45° from the horizontal ground and designed in a Perspex enclosure (of length 2·4 m, height 0·4 m and width 0·4 m). Four stabilizers were added to provide support for the beam, preventing it from deflecting under animal's weight. S1, S2 and S3 represent the beam sections within which quolls ran along the beam. Maximum sprint Maximum running speeds were elicited by placing individual quolls in a 4 × 0·6 m straight racetrack (>23 body lengths) and chasing them. The racetrack had wooden sides that were 1·2 m high to prevent escape. Individual quolls were recorded sprinting through the central 1 m section of the racetrack at least five times, using a high‐speed digital camera (Casio EX‐FH25; Shibuya, Tokyo, Japan) recording at 240 frames per second mounted above the track. Digital recordings were analysed using the video software Tracker (Open Source Physics, Boston, MA, USA) by digitizing the position of the marker frame by frame over the 1‐m section. The single fastest speed averaged across the 1 m section from all five runs was taken as an individual's maximum sprint speed, V spmax (m s−1), as per Wynn et al. (2015). Raised balance beam To assess the motor control of northern quolls, we quantified each individual's ability to traverse cylindrical beams of differing widths, a test based on standardized balance beam tests used to measure motor control in rodents (Kashiwabuchi et al. 1995; Song et al. 2006; Allbutt & Henderson 2007; Lukong & Richard 2008; Forbes et al. 2013; Welch et al. 2013; Doeppner et al. 2014; Perez‐Polo et al. 2015). Individual quolls traversed a beam (wooden dowels 2·4 m long) (>14 body lengths) that was suspended 10 cm above a Perspex base and raised at an angle of 45° (Fig. 1c). To prevent escape, the test took place in a Perspex enclosure (of length 2·4 m, height 0·4 m and width 0·4 m) with a release and escape box at the start and end respectively. As quolls could not use the slippery surface of the Perspex base, they could only get to the escape box by running along the beam. Four stabilizers connected to the central 1·35 m of the beam were anchored to the base of the enclosure, each at 0·45 m distance from one another, to provide support for the beam and to prevent bending under an animal's weight, which could create vertical oscillations and disrupt locomotion (Galvez‐Lopez, Maes & Abourachid 2012). Each stabilizer was also used as a point of reference in subsequent video analyses. Individual quolls were tested on three different beam widths; 12·5 mm (narrow), 20 mm (medium) and 30 mm (wide), each covered with sandpaper (P100 grade) to provide traction. Each trial was recorded simultaneously at 125 frames‐per‐second by two Optitrack S250e (NaturalPoint, Inc., Corvallis, OR) motion capture cameras, each placed on opposite sides of the beam. Once placed in the release box by an experimenter, individual quolls were allowed one practice walk/run on the beam to familiarize themselves with the setup before being encouraged to run in subsequent trials. This practice was enough to show the quolls that the only means of escape is to traverse the beam to the dark box at the opposite, upper end and they ran up the beam within 15 s of release into the dark box. Individuals were tested 10 times at each beam width, with a minimum of 5 min rest time between successive runs. The order of testing across the three beam widths was randomized at the start of every day to minimize the confounding effects of test order. All videos were analysed using the video software Tracker (Open Source Physics, Boston, MA, USA). Motor control was estimated for each individual for each trial by quantifying kinematic variables across the length of the full beam (1·35 m) and for each individual 0·45 m long beam section (S1, S2, S3), as marked by the position of the stabilizers (Fig. 1c). The tip of each individual's snout was used as a clear reference point and was tracked along the beam. Only those runs where the quoll immediately stepped onto the beam were included in the analyses. There were many trials on the thin beam where the quolls slipped with their first step and never recovered. Under these circumstances it was impossible to determine their running speed before the slip or whether they properly exited the box to step onto the beam. These runs were excluded from analyses. Video S1 showing successful runs by individual quolls and those involving mistakes are provided in the Supporting Information. The following data were extracted for each individual for each beam width: (i) the average section speed, V sectionave , was calculated from all trials (V section ) free of errors; (ii) the maximum section speed, V sectionmax , was taken as the single fastest section (V section ) without any slips or mistakes; (iii) the average full run speed, V runave , was calculated from trials (V run ) regardless of whether there were errors along the entire 1·35 m beam; (iv) the proportion of trials along the entire 1·35 m long beam with mistakes; (v) the proportion of all section runs with mistakes and (vi) the proportion of maximal capacity was taken as the average full run speed, V runave , divided by an individual's maximum section speed, V sectionmax . Statistical analyses A principle components analysis (PCA) was conducted on the 11 morphological variables measured using the princomp.R function in R to reduce dimensionality and search for axes of correlated variation (Table 1). PC1 explained 78·4% of the variation observed in the data and because all vectors loaded in the same direction, this represented an overall measure of body size (PC size ). PC2 explained 6·8% of the variation, and was taken as a metric of body shape (PC shape ). Table 1. Principal component loadings on 11 morphological variables measured on the northern quolls (N = 65) used in the study Variable PC1 PC2 Body length −0·31 0·05 Tail length −0·19 0·83 Tail diameter −0·23 0·42 Right foot length −0·30 −0·20 Right tibia–fibula length −0·33 −0·03 Right radius–ulna length −0·33 −0·14 Left foot length −0·30 −0·20 Left tibia–fibula length −0·33 −0·08 Left radius–ulna length −0·33 −0·13 Head width −0·32 −0·10 Head length −0·30 0·01 Proportion of variance 0·78 0·07 Data are expressed as means ± standard deviations. All statistical analyses were done in R Studio (R Studio Inc., v. 0.97.551, Boston, MA, USA, 2013) with criterion for significance in all cases being P < 0·05. All comparisons were made using ANOVA from the aov.R function in R, with individual (N = 65) and trial number included in each analysis as a random factor, and section nested within trial and individual where appropriate. Because males and females of northern quolls differ substantially in body size and life history, comparisons were made between the sexes for the following dependent variables: maximum sprint speed V spmax , maximum section speed V sectionmax , average section speed V sectionave , average full run speed V runave , probability of slipping and proportion of maximal capacity used in runs. Males did not differ from females in any performance metrics, and thus both sexes were combined in subsequent analyses (see Results section below). To explore how individual variation in morphology predicted locomotor performance (see section Is performance related to morphology? below), we first produced an individual metric of locomotor performance for quolls on the beam by running another PCA on the four performance measures: maximum section speed V sectionmax , average section speed V sectionave , average full run speed V runave and proportion of maximal capacity (Table 2). PC1 explained 81·9% of the variation observed and because all vectors loaded in the same direction, this represented an overall measure of performance on the beams (PC performance ) and was used in subsequent analyses. We assumed that quolls that had higher performances on the beams had better motor control. For each individual quoll, we calculated three PC performance values that represented performance on the large, medium and small beams, we further averaged these three values to derive a single, overall PC performance value per individual animal to use in subsequent analyses. Lengths of morphological measurements were corrected for size (body mass) by extracting residuals from a linear model of log10‐transformed lengths of each morphological measurement vs. mass. These were then compared against the four PC performance values. Table 2. Principal component loadings on four kinematic measures obtained from the northern quolls (N = 65) used in the study Kinematic measure PC1 PC2 Maximum section speed V sectionmax 0·48 −0·59 Average section speed V sectionave 0·52 −0·28 Average full run speed V runave 0·54 0·18 Proportion of maximal capacity 0·45 0·73 Proportion of variance 0·82 0·15

Results Body mass, body shape and sex Maximum sprint speed, V spmax , was not significantly correlated with body mass (F 1,60 = 0·5653, R2 = −0·007, P = 0·455) or body shape [principal component (PC) shape ; F 1,60 = 1·524, R2 = 0·008, P = 0·222]. Males had an average body mass of 471·7 ± 149·1 g, which was significantly heavier than that of females (343·1 ± 91·9 g; F 1,63 = 16·14, P < 0·001). Males also possessed significantly greater overall body sizes compared with females [principal component (PC) size ; F 1,63 = 29·34, P < 0·001]. Males did not differ from females in any performance metrics, and thus both sexes were combined in subsequent analyses (maximum sprint speed V spmax , P = 0·805; maximum section speed V sectionmax , P = 0·548; average section speed V sectionave , P = 0·162; average full running speed V runave , P = 0·195, probability of slipping, P = 0·226, proportion of maximal capacity attained, P = 0·107). Is probability of a mistake related to beam width or running speed? Quolls were more likely to slip on narrower beams (F 2,3516 = 113·44, P < 0·001), but when all beam widths were combined the probability of slipping was not affected by running speed (F 1,3516 = 1·13, P = 0·287). However, the probability of slipping was significantly affected by the interaction between the animal's running speed and beam width (F 2,3516 = 17·24, P < 0·001; Fig. 2), such that the speed at which slips occurred varied with each beam width. The probability of slipping was significantly affected by running speed over a section on narrow (F 1,590 = 17·20, P < 0·001) and medium beams (F 1,1246 = 15·44, P < 0·001), but not wide beams (F 1,1372 = 2·72, P = 0·099) (LSD and Tukey's post hoc test). Body mass was not related to the proportion of section runs with mistakes (F 1,183 = 0·02, P = 0·882) nor the proportion of trials along the entire 1·35 m long beam with mistakes (F 1,182 = 0·001, P = 0·97). Figure 2 Open in figure viewer PowerPoint Effect of animal's speed across a section, V section , on the probability of slipping across different beam widths (wide, medium, narrow). Faster speed, V section , resulted in more slips on medium and narrow beams (P < 0·001), but had no effect on wide beams (P = 0·099). The probability of slipping also increased the narrower the beam width became (F 2,3516 = 113·445, P < 0·001). Boxes represent the median with hinges representing the 1st and 3rd quartiles. Whiskers represent the 95% confidence interval (CI) and circles represent outliers. Samples sizes are shown in parentheses under each plot (***P < 0·001). Is running speed related to beam width? Quolls ran significantly more slowly across the whole beam (V runave ) as beam widths narrowed (N = 195; F 2,121 = 312·70, P < 0·001; Fig. 3a). This pattern was consistent even when only runs without mistakes were considered, with maximum (V sectionmax ) and average speeds (V sectionave ) decreasing as beam widths narrowed (both N = 189; F 2,122 = 130·9, P < 0·001 and F 2,121 = 312·7, P < 0·001 respectively; Fig. 3b,c). Speeds for those runs without slips differed among all beam widths (LSD and Tukey's post hoc test), but were not associated with body mass across any beam widths (all interactions were P > 0·05). Figure 3 Open in figure viewer PowerPoint Relationship between speed and beam widths (wide, medium, narrow). (a) Average full run speed, V runave , (b) maximum section speed, V sectionmax , and (c) average section speed, V sectionave , all of which represented measures of speed, decreased significantly as beam width narrowed. A Tukey's HSD post hoc test suggested all beam widths were significantly different from each other for all three measures of speed (significance values displayed in asterisks) (***P < 0·001; **P < 0·01). Does speed change over the beam? Modulation of running speed along the beam from sections 1 to 3 was dependent on the width of the beam (F 4,3357 = 14·87, P < 0·001; two‐way within‐subjects ANOVA). However, quolls only changed their speed along the beam when running on narrow beams (F 2,581 = 26·52, P < 0·001), not on medium (F 2,1288 = 0·19, P = 0·827) or wide beams (F 2,1494 = 0·68, P = 0·507) (LSD and Tukey's post hoc test; Fig. 4). On the narrow beams, animals were fastest during the first third of the beam (S1) and seemed to slow down for the final two sections (S2 and S3) (Fig. 1c). Figure 4 Open in figure viewer PowerPoint Speed modulation between sections within a beam across three beam widths (wide, medium, narrow), when only animal's successful runs (P slip = 0) were considered. Animals modulated their speed across all sections only on narrow beams (F 2,641 = 19·76, P < 0·001), and not on wide and medium beams (significance values displayed in asterisks) (***P < 0·001). S1, S2 and S3 represent the section numbers within which quolls ran along the beam. Does performance improve with practice? Average speed across the entire beam (V run ) varied with trial number (F 1,053 = 51·86, P < 0·001) and beam width (F 2,1053 = 559·97, P < 0·001). With practice, quolls’ V run became significantly faster on wide (F 1,414 = 24·06, P < 0·001) and medium beams (F 1,383 = 17·82, P < 0·001), though speeds did not improve on narrow beams (F 1,307 = 2·24, P = 0·135) (Fig. 5). Figure 5 Open in figure viewer PowerPoint Presence of motor learning based on improvement in animals’ speed along the entire beam, V run across three beam widths (wide, medium, narrow). Quolls showed significant motor learning on wide (F 1,414 = 24·06, P < 0·001) and medium beams (F 1,383 = 17·82, P < 0·001) (solid lines), but not on narrow beams (F 1,307 = 2·242, P = 0·135). P‐values are shown under each plot. How does slipping affect running speed? When quolls slipped on the beam, their speeds were decreased over the entire beam length. Individuals that had a higher probability of slipping during their 10 trials had slower average running speeds (normalized to their maximum section speed) (F 1,118 = 274·59, P < 0·001). This indicates that those individuals that slipped more frequently took longer to run the entire length of the beam – with individuals that slipped on each run taking around twice as long to run the length of the narrowest beam than those that did not slip. Because average speeds were normalized to each individual's maximum speed on each beam, the lower average speeds for individuals that exhibited higher frequencies of slips could be used to estimate the costs of slipping on speed. However, this negative correlation varied among beam widths (F 2,118 = 7·74, P < 0·001). There was a significant negative correlation between probability of slipping and average relative running speed for the narrow beam (F 1,59 = 23·14, R2 = 0·267, P < 0·001), but not the medium (F 1,61 = 3·59, R2 = 0·040, P = 0·063) or wide beam (F 1,62 = 2·28, R2 = 0·020, P = 0·136) (Pearson's correlation coefficients) (Fig. 6). Figure 6 Open in figure viewer PowerPoint The negative correlation between probability of slipping and average proportion of maximal capacity attained by an animal across three beam widths (wide, medium, narrow). The proportion of maximal capacity attained, used as a proxy of cost for mistakes was significantly correlated to the probability of slipping (F 1,182 = 261·553, P < 0·001) and beam width (F 2,182 = 9·176, P < 0·001). When analysed separately, the interaction was only significant on narrow (F 1,59 = 23·14, R2 = 0·2695, P < 0·001) beam, not wide (F 1,62 = 2·276, R2 = 0·0198, P = 0·1365) and medium (F 1,61 = 3·587, R2 = 0·0400, P = 0·063) beams. Is performance related to morphology? We found no correlation between an individual quoll's metric of overall performance (PC performance ) with body mass and overall body size (PC size ) at all three beam widths (all interactions were P > 0·05). There was also no correlation between an individual's overall body shape (PC shape ) with a metric of overall performance (PC performance ) for the medium and widest beam, but there was for the narrowest beams (F 1,63 = 5·453, P < 0·05). As this correlation suggested morphological variables to be significantly correlated with the animal's performance, we explored the effect of body shape on locomotor function using multiple linear regressions and found that longer tail length was associated with better performance on wide beams only (P = 0·002).

Discussion We ran wild‐caught northern quolls (D. hallucatus) along beams of varied widths to test key assumptions and predictions of Wheatley et al.'s (2015) model of optimal escape speeds. We found that quolls were more likely to slip when they ran faster, and that the probability of slipping was greater on narrower beams. Slipping also increased the total time it took a quoll to traverse the beam, with each mistake on the narrowest beam decreasing average speeds over the 1·35 m beam by around 50% – a substantial cost. To circumvent the costs of mistakes, quolls reduced speeds in situations when they were more likely to make a mistake (i.e. narrower beams). Our data provide support for the assumptions underlying Wheatley et al.'s (2015) model of optimal escape speed because both the accuracy of foot placements decreasing at faster running speeds and subsequent mistakes were costly. Taken together, our study suggests animals may optimize their speeds when running away from predators – at least along linear habitats where there is a clear speed‐accuracy trade‐off – and that animals should select their escape strategy based on compromises between speed of movement and motor control. Running faster increased the probability of slipping on only the two narrower beams, with quolls slipping on 10% of all sections of the narrowest beams. These data show that the cost for high‐speed movements is a loss of accuracy of limb placement and that when task difficulty is greatest the compromise in limb accuracy results in more mistakes. The higher number of slips observed on the narrowest beam allowed us to estimate how average speeds were slowed by mistakes. We found a negative correlation between the proportion of runs in which individuals made mistakes and their average running speed along the entire beam, but this was only on the narrowest beam. Using the slope of this negative correlation, we could estimate that each mistake was associated with a 50% decrease in average speed. In other words, northern quolls that slipped on every run had average speeds (as a proportion of each individual's maximum) over the narrow beam that were half those of animals that did not slip. This is not a trivial cost, and is likely an enormous incentive for animals to decrease their running speeds along branches and other challenging substrates to minimize mistakes. We expected that quolls would modulate their speed to decrease their probability of mistakes. Supporting this idea, we found that an average speed for quolls when running along the narrowest beams was around 40% lower than when moving on the widest beam. These data offer support for a key prediction of the mathematical model of Wheatley et al. (2015) that animals should not simply move at maximum speeds, but should choose the speed that maximizes their probability of survival, given the conditions. We found that quolls slowed down even across the length of the narrowest beam, running fastest during the first third (S1; Fig. 1c) and slower on the second and last thirds (S2 and S3). This kind of fine‐tuning was not observed on the two wider beams, suggesting that quolls were quick to find optimal speeds on the larger beams or were more willing to accept the consequences of misjudgement. Our results are consistent with those reported for several species of Anolis lizards that slow their running speeds when on narrower beams (Losos & Sinervo 1989; Losos & Irschick 1996; Irschick & Losos 1999). For example, the running speed of Anolis gundlachi was 45% lower when running on a 1·2 cm wide beam compared with speeds on a 4·5 cm wide beam (Losos & Sinervo 1989). We found the running speeds of northern quolls increased over the 10 trials on both the wide and medium beams but not on the narrow beam. This motor learning on the medium and wide beams is similar to that reported for rodents on balance beam tests (Kashiwabuchi et al. 1995; Scherbel et al. 1999; Shear et al. 2010). For example, Kashiwabuchi et al. (1995) reported both GluR82 mutant and wild‐type mice, which were given four trials per day for five consecutive days, showed a clear decrease in the number of slips after a few trials on the balance beam test. In contrast, the lack of improvement in performance on the narrow beam suggests that this motor task is more difficult and thus requires much greater practice before learning begins to take effect on running speeds. The improved running speeds on the wide and medium beams with practice may be due to improved motor function or better speed selection that minimizes the probability of mistakes. Regardless, the varied trajectories of motor learning that are associated with task difficulty provides an interesting experimental system for future studies exploring motor learning in a wild mammal. Our understanding of how animals select their movement speeds in nature is clearly in its infancy. Studies exploring the costs of high‐speed movements provide a necessary counterbalance to those quantifying the benefits of mistake‐free movements for task success and are critical for exploring how animals decide what speeds to use in nature. Although the concept of optimal performance has received very limited attention (Clemente & Wilson 2015a, b; Wilson et al. 2015), we believe it will provide the foundation for understanding the movement behaviour of animals. We now know that high‐speed movements compromise both the motor control (this study) and manoeuvrability (Wynn et al. 2015) of northern quolls and make locomotor mistakes more likely. Increased probabilities of mistakes represent one of the most important costs for high‐speed movement. But despite the importance of mistakes, functional biologists rarely quantify motor mistakes (Wilson et al. 2014, 2015). In order to advance our ability to predict the movement speeds of animals, we need to begin to study animal mistakes in a more direct, quantitative way using both laboratory and field analyses. Future studies should also attempt to fully parameterize optimality models so as to provide a quantitative rather than just a qualitative prediction for an individual's escape running speed. This will require data for both a prey's speed–accuracy trade‐off as well as those for the predators, because it is the interaction between these two agents that will determine the specific optimal escape speed for prey. Because mistakes can also vary in their costs – or probability of leading to task failure – we need to understand the relationship between the types and magnitudes of mistakes and how they affect task success. Studies exploring animal speed choice offer exciting opportunities for learning more about a basic decision made by all animals across almost all behavioural contexts.

Authors’ contributions A.F.A.A.N., M.W. and R.S.W. conceived the ideas and designed methodology; A.F.A.A.N., C.C. and M.W. collected the data; A.F.A.A.N. and C.C. analysed the data; A.F.A.A.N., C.C. and R.S.W. led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

Acknowledgements We thank members and volunteers of the Wilson Performance Lab for assistance with running the experiments and Amanda C. Niehaus for assistance with editing the manuscript. We also thank the Anindilyakwa Land and Sea Rangers of Groote Eylandt for their generous assistance, logistical support and use of laboratory facilities. We also thank the traditional owners of Groote Eylandt for their generous support and access to their land. This project was supported by the Anindilyakwa Land Council, a University of Queensland collaboration and Industry Engagement Fund (UQ‐CIEF) grant awarded to R.S.W., an Australian Research Council (ARC) Discovery Grant awarded to R.S.W. and an ARC Discovery Early Career Research Award (DECRA) grant awarded to C.C.

Data accessibility All data are available in the Dryad Digital Repository https://doi.org/10.5061/dryad.2dh6m (Amir Abdul Nasir et al. 2017).

Supporting Information Filename Description fec12902-sup-0001-Laysummary.pdfPDF document, 110.3 KB Lay Summary fec12902-sup-0002-VideoS1.m4vvideo/x-m4v, 2.7 MB Video S1. Examples of running trials by individual quolls on the beams, showing both the successful runs and those involving mistakes. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.