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Article

Biomechanical Trade-Offs Between Speed and Agility in the Northern Brown Bandicoot

by
Kaylah Del Simone
1,
Skye F. Cameron
1,2,
Christofer J. Clemente
3,
Taylor J. M. Dick
4 and
Robbie S. Wilson
1,*
1
School of the Environment, The University of Queensland, St. Lucia, QLD 4072, Australia
2
Australian Wildlife Conservancy, Mornington Wildlife Sanctuary, Subiaco, WA 6728, Australia
3
School of Science, Engineering and Technology, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
4
School of Biomedical Sciences, University of Queensland, St. Lucia, QLD 4072, Australia
*
Author to whom correspondence should be addressed.
Biomechanics 2025, 5(3), 52; https://doi.org/10.3390/biomechanics5030052
Submission received: 30 May 2025 / Revised: 14 July 2025 / Accepted: 14 July 2025 / Published: 17 July 2025
(This article belongs to the Section Sports Biomechanics)
Browse Figures
Versions Notes

Abstract

Background/Objectives: Australian terrestrial mammals that fall within the critical weight range (35 g–5.5 kg) have experienced large population declines due to a combination of habitat loss and modification, and the introduction of non-native cats, dogs, and foxes. Because running speed typically increases with body size, predators are usually faster but less agile than their prey due to the biomechanical trade-offs between speed and agility. Quantifying the maximum locomotor capacities of Australian mammals in the critical weight range, and the magnitude of the trade-off between speed and agility, can aid in estimating species’ vulnerability to predation. Methods: To do this, we quantified the trade-off between speed and agility in both males and females (n = 36) of a critical weight range species, the northern brown bandicoot (Isoodon macrourus), and determined if there was an influence of morphology on locomotor performance. Results: When turning, individuals who had higher turn approach speeds, and higher within-turn speeds, had greater turning radii and lower angular velocities, meaning a decrease in overall maneuverability. Females were more agile and exhibited greater turning speeds at similar turning radii than males. For both sexes, individuals with longer relative hind digits had relatively faster sprint speeds, while those with longer forearms had relatively smaller turning radii and higher agility. Conclusions: Due to the constrained limb morphology of the bandicoot species, these findings could translate across this group to provide a better understanding of their escape performance and risk of predation.

1. Introduction

Predation is one of the most pervasive ecological factors affecting communities—from the behaviour of individual organisms through to broad-scale changes in species assemblages [1]. For example, predation by non-native species is one of the leading contributors to the decline of many Australian mammals [2,3]. Ground-dwelling mammals weighing between 35 g and 5.5 kg have been disproportionally affected by predators compared to those that do not fall within this size range [2]. Species within this group, labelled the ‘critical weight range’ (CWR), are highly vulnerable to two prominent invasive predators, the fox, Vulpes vulpes, and domestic cat, Felis catus, due to their small size, non-arboreal lifestyle, and nocturnal activity [4]. In addition, as cats and foxes have only been recently introduced, native Australian species are being exposed to evolutionarily novel situations that may not elicit appropriate predator recognition and anti-predator responses [5]. A detailed analysis of how predators capture prey, such as how they utilize their habitat or via characterizing differences in locomotor performance, can ultimately provide fundamental insights into how communities function [1] and can help make predictions about the future impact of predators on their prey.
Predation can be avoided in two broad ways: avoiding detection or escaping capture once detected [6,7,8]. Once an animal has been detected by a potential predator, escape success is determined by the relative performance of predator and prey [9,10,11]. However, predicting the outcome of predator–prey interactions requires moving beyond simple trophic niche concepts, which typically assert that if an animal falls within a certain size range, it can be considered potential prey for a larger predator [9,10]. In reality, the relative speed and agility of a particular prey species may be sufficient to enable escape, even if it resides within the predator’s ‘optimal’ size range [10]. Therefore, understanding the specific mechanisms underlying predation—such as relative performance at the individual level—can enhance our ability to predict the outcomes of both highly specific and generalized interspecific interactions [9,11]. This knowledge can be used to inform current, or develop future more effective and novel, management strategies.
Research determining which strategies increase escape success has largely focused on the maximum locomotor capacities of prey species, with maximum sprint speed being one of the most common traits measured [12,13,14,15]. This is perhaps not surprising given that faster sprinters should be more likely to escape predation than slower individuals, and equally, faster predators should be more likely to capture prey [12,13,14,15]. Rarely, however, do species solely rely on their maximum sprinting speeds to escape a predator. In a pursuit scenario, prey species commonly employ a range of tactics—such as abrupt changes in direction, bursts of acceleration or deceleration, and strategic movement choices—to increase their probability of escape. [16,17]. By quantifying an animal’s ability to make rapid changes in direction, often defined as agility [18,19], in combination with information regarding maximum speed, one can provide a more accurate assessment of the escape probabilities of prey relative to their specific predators [19]. In general, higher agility is categorized by faster turning speeds at smaller radii, while animals with lower agility turn at slower speeds and large radii [20]. The biomechanical demands of turning require increased centripetal and tangential forces, maintenance of sufficient friction between the feet and substrate to avoid slipping, and leaning inwards to counter the ground reaction forces without tipping [19,20,21,22]. Faster speeds going into a turn result in greater centripetal forces and a reduced frictional limit, meaning more often than not, the animal must decrease its turning speed or perform a larger turn in order to avoid mistakes such as losing their footing [16,17,22]. Northern quolls, Dasyurus hallucatus, for example, decrease their maximum sprint speed by 35% to avoid slipping during a sharp turn [22].
Biomechanical constraints limit the ranges of speeds at which animals can move [20,23,24,25] and how their morphology and physiology affect their performances [20,22,26,27]. Limbs need to support the weight of an animal, resist the stresses associated with locomotion, and facilitate a range of movement behaviours [25,28,29]. In quadrupedal animals, these stressors act in different magnitudes on each limb. While the forelimbs are often the principle support for maneuverability and a range of diverse behaviours like digging, foraging, and climbing [25,30], the hindlimbs are primarily used to produce the forces required for locomotion [31,32]. The production of forces must then outweigh frictional constraints of movement, or a limb’s resistance to linear acceleration through space [33], both aspects which are determined by limb size and shape. For example, longer limbs may promote larger muscle attachment sites and contribute to a greater ability to produce acceleration [31]; however, if the mass of distal limb elements is too great, more energy may be expended for little gain [25,34]. Therefore, by measuring an individual’s limb shape, we can make assumptions about their specific locomotor capabilities, such as sprint speed or turning ability [35,36].
Peramelidae are a unique and diverse group of marsupials [2]. Over half of the Australian species of Peramelidae are either extinct or threatened with extinction primarily due to a combination of habitat destruction and predation from non-native predators [5,37,38,39]. Species in this group have highly distinct limb morphology with a fully ossified patella, a fibula that does not articulate with the femur at the knee [31], and the lack of a clavicle [31,40]. In addition, their forelimbs are structurally and functionally different from their hind limbs [40]. Their short and robust forelimbs reflect functionality for subterranean foraging [41], which is distinct from the cursorial adaptations of their hind limbs that are elongated and have a reduced hind digit number [31,32,42,43]. As these morphological differences influence how limbs move in space, locomotor behaviours can be impacted—such as the lack of clavicle in this group potentially contributing to a greater range of motion available to their forelimbs, while limiting abduction and rotation ability [44,45]. That is, due to changes in the way the limbs move, making predictions of locomotor performance based on relative sizes and speeds alone may be unreliable [46,47]. Because bandicoots also have relatively short forelimbs, their turning ability is likely to be substantially compromised at high speeds.
In this study, we examined how the behaviour and morphology of male and female northern brown bandicoots (Isoodon macrourus) drive the biomechanical trade-off between running speed and turning ability. For each individual, we measured morphology, maximum sprint speed along a straight runway, and their ability to change directions at various speeds in a large outdoor arena. First, we explored how an individual’s running speed affected their agility when they do choose to make a turn. We predicted that as the running speeds immediately preceding a turn increase then an individual’s turning radius would also increase, and thus indicative of a lower agility. Second, we examined how individual variation in morphology affected the trade-offs between speed and agility. We predicted individuals with longer hind limbs would have higher maximum sprint speeds but lower agilities, while individuals with longer forelimbs would have greater agilities but lower maximum sprint speeds. Because males are larger than females, we also expected males to be faster than females, but for females to have greater agility.

2. Methods

2.1. Field Work

Trapping was conducted on Groote Eylandt, NT, Australia, across four different sites. Each site consisted of a fixed trapping grid (450 × 300 m) comprising four transect lines that were 100 m apart, with 10 traps per transect spaced at 50 m intervals. Animals were caught using Tomahawk original series traps (14 × 14 × 45 cm; Tomahawk ID-101SS, Hazelhurst, WI, USA) baited with tinned dog food, set at dusk, and checked at dawn. Each site was trapped for five consecutive nights to ensure all individuals in the area had been caught at least once. Upon capture, animals were transferred to individual calico bags (550 × 750 mm; Westernex SKU:B53, Brisbane, Australia) and transported to the Anindilyakwa Ranger Research Station, where they were fitted with a numbered ear-tag (National Band 1005-1, Newport, KY, USA) for future identification purposes. Thirty-six northern brown bandicoots (I. macrourus) were collected from September to October 2019. All animal handling and experiments were performed in accordance with our research ethics protocol approved by the University of Queensland’s Native/Exotic Wildlife and Marine Animals (NEWMA) ethics committee (NEWMA approval number: SBS/300/19/NT).
The body mass (±1 g) of each individual was measured using electronic scales (±0.1 g; A and D Company Limited HL200i, Brisbane, QLD, Australia) and morphological variables were recorded using digital callipers (Whitworth, Brisbane, Australia; ±0.01 mm). Morphological measurements included head length (from nuchal crest to tip of snout), head width (widest point of jaw), fore and hindfoot digit length (claw base to foot pad), fore and hind full foot length (foot pad to heel), fore and hindlimb lengths (radius to ulna and tibia to fibula, respectively) (Figure 1). Body length (nuchal crest to base of tail) was recorded using tailors’ tape (±0.01 mm). Limb measurements were taken on both left and right sides, and for each morphological characteristic, the average of three measures was taken. Age was determined by size, reproductive stage (determined by testes colour and size in males, and pouch development and presence of pouch young in females), as well as molar wear.
To create an overall metric of body size, we conducted a principal component analysis (PCA) using all morphological variables. The first principal component, PCbodysize, described >90% of the variation and all variables loaded in a positive direction. Each of the remaining components accounted for <5% of the variation in the data and were excluded from further analyses.
All locomotor performance assessments were conducted in an outdoor enclosure to avoid the influence of an altered physical and sensory environment on the performance of the individuals. The enclosure consisted of a 3 × 1 m rectangular runway, leading into a 3 × 3 m square arena, constructed from two layers of shade cloth (50%, 3.66 × 30 m 130 gsm, LifeAU, Brisbane, Australia) pegged into the ground, with an open and empty Elliot trap in each corner of the arena. The ground (compacted soil) was left unaltered to retain natural ground cover, with the exception of removing large shrubs or rocks that impeded movement. A high-speed digital camera (Casio EX-FH25, Toyko, Japan) was used to record the performance of the bandicoots at 240 Hz, with the camera positioned 3 m above the ground, directly above where the runway meets the arena.
Each bandicoot had a 2 × 2 cm marker, made from a square of masking tape, affixed to their back between their shoulder blades to aid in the analysis of the footage. To elicit maximum sprinting speeds, individuals were placed at the closed end of the runway and encouraged to sprint into the open arena by shaking a cloth bag behind its rear. A total of at least three straight maximum sprints were recorded, defined as when an animal deviated no more than 15 degrees along its trajectory path. In addition to straight line speed, at least six agility trials were conducted. For the agility trials, a researcher positioned at the opening of the runway passively encouraged the bandicoot to turn away from the person when sprinting out of the runway and into the broad square arena. By changing the position of a second researcher, animals were encouraged to elicit cornering in the left and right directions. Trials in which the bandicoot did not run continuously along their path were not used in subsequent analyses.
Data were extracted from the video footage using the video software Tracker (Version 4.87, Open-Source Physics, Boston, MA, USA), following a modified method from Wynn et al. [22]. The position of the marker on the animal was tracked from the start of the runway opening, to the completion of the run when the animal was out of the frame. For the three sprints, the x/y axis positions of the marker, time (s), and speed (m.s−1) were extracted from each run. For the agility trials, the x/y positions of the marker were extracted and using custom-written scripts (R2016b, MathWorks, Natick, MA, USA) these positions were smoothed by a mean square algorithm (tolerance = 0.05 error in pixels frame − 0.1). The following variables were then calculated: (i) average pre-turn speed in the stride immediately preceding the turn (m.s−1), (ii) average turning speed throughout entire turn (m.s−1), (iii); radius of the turn (m) determined by a circle fitted to the positional data around the turn using the least squares modelling approach, as per Wheatley et al. [11], and (iv); average angular velocity of the turn (degrees.s−1).

2.2. Statistical Analyses

Differences in overall body sizes were compared between the sexes using Welch’s t-test (PCbodysize). To determine if there were sex differences in the relationship between body size and individual morphological traits, we examined the allometric scaling relationships of all morphological traits on body size. We used standard major axis (SMA) regression to assess the static allometry (comparisons of a population within the same developmental stage) to provide a description of the differences in morphology due to sexual dimorphism. This was carried out using the SMATR package v3.4 (https://www.rdocumentation.org/packages/smatr/versions/3.4-3, accessed on 30 May 2025), with α = 0.05, and H0 slope = 1 (F-test). All relative morphology traits were log-transformed prior to analysis to ensure normality.
To remove the effect of size, the residuals of SMA regressions between each morphological variable and overall body size were used. These measurements are henceforth referred to as relative measures. An individual metric of agility was also produced by using the residuals from the log-linear relationship between turning speed and turning radius, assuming animals that can turn faster at any given radius have a higher average agility. Initially, generalized linear mixed-effect models (GLMMs) were created to test the relationship between sprint speed, turning speed, turning radius, pre-turn speed, and angular velocity. Then, the effect of each individual morphological trait on performance was determined using GLMMs. In these models, sex was also used as an interaction effect with each morphological trait to determine if these relationships differed between males and females. Where needed, continuous variables were log-transformed to meet assumptions of normality. All full models were simplified using conditional model averages through the MuMIn package v1.15.6 (https://CRAN.R-project.org/package=MuMIn, accessed on 30 May 2025), using Akaike weights of ≥0.01. After the best fit model was found for all variables, statistically significant correlations were determined using ANCOVA. Individual was used as a random factor in each analysis and multicollinearity was assessed using car v3.0-10 (https://CRAN.R-project.org/package=car, accessed on 30 May 2025), to ensure final models had acceptable variance inflation factors (VIF < 10). All analyses were conducted in the R statistical software environment v3.6.2 (https://www.r-project.org/, accessed on 30 May 2025), using the lme4 package v1.1-26 (https://cran.r-project.org/package=lme4, accessed on 30 May 2025) to construct GLMMs.

3. Results

Although an increase in an individual’s average sprint speed tended to be associated with a decrease in their average agility, this was non-significant (F1,32 = 3.16, p = 0.085). When an individual turns, higher pre-turn speeds were likely to coincide with faster speeds during the turn (F1,81 = 47.66, p < 0.001, Table 1, Figure 2A) and an increase in the turning radius (F1,78 = 5.79, p = 0.018, Table 2, Figure 2C). Turning speed was also positively associated with turning radius (F1,99 = 130.35, p < 0.001, Table 1, Figure 2B). Angular velocity, however, had a negative association with both turning speed (F1,85 = 58.78, p < 0.001, Table 3, Figure 2C) and turning radius (F1,99 = 169.68, p < 0.001, Table 3, Figure 2F). At low turning speeds, the turning radius was likely to decrease further if it coincided with a lower angular velocity (F1,96 = 9.216, p = 0.003, Table 2) or a faster pre-turn speed (F1,96 = 5.788, p = 0.018, Table 2). However, the opposite was more likely at faster turning speeds; a high angular velocity or a slow pre-turn speed was associated with a decrease in turning radius.
Adult male bandicoots (ages 1–4 years, 1151.6 ± 419.5 g) were significantly larger than females (ages 1–4 years, 817.4 ± 117.6 g) (F1,34 = 10.144, p = 0.0031). In addition, there were sex differences in the scaling relationship between specific morphological variables (Table 4). The relationship between each morphological variable and body size was categorized as either positively or negatively allometric, or isometric. Males and females differed in the allometric scaling for six of the nine measured morphological traits (hind-digit, palm, foot, forelimb, hindlimb and head length), with females having significantly higher slope values (the trait had a greater increase in size per unit increase in body size) in all but hind-digit length, which showed negatively allometric scaling in females (slope = <1). In contrast, head width, fore digit, and body length had similar allometric scaling relationships between the sexes (all positively allometric). Both fore- and hind-digit measures had a significant difference in slope, with females having a greater increase than males, meaning fore- and hind-digits are relatively larger in similarly sized females than males.
Maximum sprint speed of male bandicoots was 3.0 ± 0.8 m s−1, which was not significantly different to females (2.9 ± 0.8 m s−1) (F1,30 = 1.110, p = 0.301) (Figure 3A). However, females had significantly greater turning speeds (2.3 ± 0.5 m.s−1) than males (2.1 ± 0.5 m.s−1) (F1,32 = 6.195, p = 0.018) (Figure 3B). When running around corners, the angular velocity of bandicoots was 70 ± 36 degrees.s−1, which did not differ between the sexes (F1,28 = 2.862, p = 0.102; Figure 3C). Although the average pre-turn speed (F1,32 = 3.735, p = 0.062) (Figure 3D) and turning radius were both higher in females, these differences were non-significant (F1,31 = 3.550, p = 0096) (Figure 3E). Females had a pre-turn speed of 2.2 ± 0.6 m.s−1 and males averaged 1.9 ± 0.5 m.s−1, while females had turning radii of 3.3 ± 1.1 m and males averaged 3.1 ± 0.8 m.
Sprint speed was positively correlated with hind digit length (F1,34 = 9.733, p = 0.004), but negatively with hind foot length (F1,35 = 7.353, p = 0.010). Thus, individuals with longer hind digit lengths and shorter foot lengths were likely to be faster. The approach speed of bandicoots was negatively correlated with head width (F1,47 = 7.493, p = 0.009) and length (F1,37 = 8.755, p = 0.005). That is, individuals with wider and longer heads were more likely to approach turns at lower speeds. Further, bandicoots were more likely to have slower turning speeds if they had longer heads (F1,31 = 6.416, p = 0.017). While the relationship between turning speed and palm length was non-significant on its own (F1,31 = 0.371, p = 0.547), palm length was positively associated with angular velocity and agility for both sexes (F1,28 = 9.702, p = 0.004; F1,29 = 7.822, p = 0.009). Although agility was negatively associated with head length for both males and females (F1,34 = 12.155, p = 0.001), females showed a greater relative decrease in agility with increases in head length (F1,35 = 4.746, p = 0.036). Agility was also only positively associated with forelimb length for females. (F1,36 = 5.713, p = 0.022). Finally, the turning radii was likely to decrease as the length of the forelimb increased (F1,33 = 4.876, p = 0.034).

4. Discussion

We examined how behaviour and morphology influence the biomechanical trade-off between running speed and turning ability in male and female northern brown bandicoots. Since higher running speeds generate greater centripetal forces during turns and approach the limits of available friction [16,17,22], we predicted that increased speeds would result in larger turning radii. Our findings supported this hypothesis, as higher pre-turn speeds were associated with larger turning radii and decreased angular velocities. Similarly, Wynn et al. [22] observed that northern quolls (Dasyurus hallucatus) reduce their maximum sprint speed when turning to avoid slipping [22].
Female bandicoots are smaller and more agile than males, although both sexes showed similar maximum sprinting speeds. Females demonstrated higher turning speeds while maintaining comparable turning radii. This increased agility in females is likely attributed to their smaller body size, which results in lower angular momentum. Additionally, females showed greater relative increases in turning speed with increases in forearm length. During high-speed turns, animals experience increased inertia due to the change in the direction of motion [48]. This can be overcome by producing greater stabilizing forces from the limbs. In quadrupedal animals, turning is mainly facilitated by using the forelegs to generate forces perpendicular to the direction of motion [49]. Since bandicoots have a largely abducted limb posture, restricted elbow movement, and no clavicle [41], longer forelimbs relative to body size would allow for a greater range of forelimb movement and would improve turning ability. Given that adult female bandicoots have proportionally longer forelimbs, this anatomical feature likely enables them to make sharper turns with lower energy costs per unit distance, enabling faster and more agile movement compared to males.
Both sprint speed and agility were influenced by individual morphological variation. Bandicoots with longer toes on their hind limbs exhibited relatively faster sprint speeds, while those with larger foot pad areas on their hind limbs had slower sprint speeds. The elongation of the frontal elements of the feet (carpals and tarsals) has been linked to increased sprint speed in other mammal species [50,51,52,53,54]. Longer load-bearing digits provide greater ground contact time, which enhances propulsive impulse during acceleration [55]. In contrast, elite 100 m sprinters with longer toes tend to sprint faster, while those with longer plantar flexor muscle moments perform worse in sprinting [56]. Shorter plantar flexor moment arms or heel lengths in these sprinters facilitate greater force production during acceleration [56]. While sprint speed and agility were related, bandicoots with longer palm lengths showed greater agility and angular velocities, whereas longer forearm lengths were associated with smaller turning radii. Agility likely increases due to a larger footpad area in contact with the ground during turns, which may reduce frictional limitations on turning speed. Additionally, bandicoots with larger heads were significantly slower in approach speed, turning speed, and agility. This could be due to the increased anterior weight caused by a larger head, which may impair turning performance, similar to patterns observed in some lizards [27,57]. This effect may be amplified in bandicoots, which already have a highly anterior centre of gravity [58].
In contrast to the shortened forearm of bandicoots, which are adapted for fossoriality, the hind limbs of all Peramelidae species are designed for rapid propulsion. These limbs have large thigh muscles and reduced, non-weight-bearing digits, along with elongation of the weight-bearing digits [31]. In our study, the average sprint speed of bandicoots was 2.97 m/s, with the fastest individual, a first-year male, reaching 5.0 m/s. The average turning speed did not significantly differ from maximum sprint speed, but the fastest turning speed observed was 3.67 m/s. Although the maximum sprint speeds in our study were higher than previously reported for this species (5 vs. 4 m/s) [59], northern brown bandicoots are still much slower than similarly sized half-bounding lagomorphs (e.g., rabbits and hares), which can reach speeds up to 13.9 m/s [12]. The relatively short forearms of bandicoots, coupled with their highly anterior posture and centre of gravity, may limit their maximum sprint speeds [40,58].
Over a third of modern extinctions have occurred in Australia [59], and the continued loss of endemic species poses a significant threat to global biodiversity [3]. Nearly half of Australia’s endemic land mammals are at risk or already extinct due to habitat loss, land-use changes, and the introduction of predators such as foxes and cats, which consume large numbers of native animals [3,60]. In northern Australia, feral cats are driving small mammal populations to extinction, particularly in flat, open areas like grasslands and recently burned or pastured landscapes, where prey is more exposed and easier to catch [61]. Large-scale, catastrophic fires can destroy the fallen timber and understory that provide refuge, increasing the likelihood of predator-prey encounters [62,63]. Despite these threats, animal performance is rarely studied in an ecological context, leaving conservationists without effective tools to predict which species are most likely to survive such encounters. Understanding extinction risk requires identifying when, where, and how predators capture prey—and how prey evade them. The outcome of these interactions is shaped by the physical and performance traits of both predator and prey, including body size, speed, and agility. Because their movements are dynamic and interdependent—further influenced by environmental obstacles—studies of animal performance should account for these interactive, real-world conditions.
To predict vulnerability or extinction risk more accurately, it is essential to identify the physiological, behavioural, and environmental traits that enable prey to escape predation. Focusing solely on one aspect, such as sprint speed, is insufficient. A more holistic approach—considering multiple dimensions like acceleration and agility—provides better insight into which species are likely to survive predator encounters and which are most at risk [36]. Given that over a third of modern extinctions have occurred in Australia [59], refining our understanding of performance-related survival is critical for biodiversity conservation [3,60]. Incorporating performance studies into conservation biology allows for a more comprehensive understanding of how species cope with predation in changing environments. To expand on this, future studies could adopt a more detailed approach than the one used here. While efforts were made to preserve natural habitat conditions, microhabitat variability can still influence animal performance and behaviour [64]. Simulating predator evasion in varied environments could yield deeper insights. Additionally, increasing sample sizes would allow for a better understanding of intra-specific variation, further informing conservation efforts.

Author Contributions

Conceptualization, K.D.S., S.F.C., C.J.C., T.J.M.D. and R.S.W.; methodology, K.D.S., S.F.C., C.J.C., T.J.M.D. and R.S.W.; software, R.S.W.; validation, K.D.S., S.F.C., C.J.C., T.J.M.D. and R.S.W.; formal analysis, K.D.S. and S.F.C., investigation, K.D.S. and S.F.C.; resources, C.J.C., T.J.M.D. and R.S.W.; data curation, K.D.S. and R.S.W.; writing—original draft preparation, K.D.S., S.F.C., C.J.C., T.J.M.D. and R.S.W.; writing—review and editing, K.D.S., S.F.C., C.J.C., T.J.M.D. and R.S.W.; visualization, K.D.S.; supervision, C.J.C., T.J.M.D. and R.S.W.; project administration, S.F.C. and R.S.W.; funding acquisition, R.S.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Australian Research Council Fellowship (FT150100492).

Institutional Review Board Statement

The study was conducted in accordance with the Animal Ethics and Welfare Committee of The University of Queensland (protocol code: SBS/300/19/NT and date of approval: 2017).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available upon request from the corresponding author.

Acknowledgments

We thank all the volunteers who helped with the collection of the data. RSW was supported by an Australian Research Council Fellowship (FT150100492).

Conflicts of Interest

The authors declare no conflicts of interest.

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Biomechanics 05 00052 g001
Figure 1. Morphological measurements taken from each individual northern brown bandicoot (Isoodon macrourus).
Figure 1. Morphological measurements taken from each individual northern brown bandicoot (Isoodon macrourus).
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Figure 2. The relationship between (A) pre-turn speed, (B) turning radius, and (C) angular velocity on turning speed, and the relationship between (D) pre-turn speed, (E) turn speed, and (F) angular velocity on turning radius, for 31 northern brown bandicoots (Isoodon macrourus). Pre-turn speed refers to the speed in the stride immediately preceding the turn (m.s−1). Turning radius refers to the radius taken from a circle fitted to the positional data around the turn. Angular velocity is that recorded during the turn (degrees.s−1).
Figure 2. The relationship between (A) pre-turn speed, (B) turning radius, and (C) angular velocity on turning speed, and the relationship between (D) pre-turn speed, (E) turn speed, and (F) angular velocity on turning radius, for 31 northern brown bandicoots (Isoodon macrourus). Pre-turn speed refers to the speed in the stride immediately preceding the turn (m.s−1). Turning radius refers to the radius taken from a circle fitted to the positional data around the turn. Angular velocity is that recorded during the turn (degrees.s−1).
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Figure 3. The relationship between body mass and (A) maximum sprint speed, (B) maximum turning speed (C) average cornering velocity, (D) average approach speed, and (E) turning radius for individual male (light blue circles) and female (light red circles) northern brown bandicoots (Isoodon macrourus).
Figure 3. The relationship between body mass and (A) maximum sprint speed, (B) maximum turning speed (C) average cornering velocity, (D) average approach speed, and (E) turning radius for individual male (light blue circles) and female (light red circles) northern brown bandicoots (Isoodon macrourus).
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Table 1. Two-way ANOVA for best GLM showing relationship between turning speed and other performance variables.
Table 1. Two-way ANOVA for best GLM showing relationship between turning speed and other performance variables.
Sum SqMean SqNum DfDen DFFp
Radius1.1591.159198.877130.345<0.001
Angular velocity0.3920.392198.70134.966<0.001
Pre-turn speed0.2340.534181.32047.657<0.001
Linear mixed effect models fit by REML; ID was used as a random fixed effect. Observations = 106, N = 36. DenDF (denominator degrees of freedom, NumDF (numerator degrees of freedom). SumSq (sum of squares), MeanSq (mean of squares). F-values are indicated.
Table 2. Two-way ANOVA for best GLM showing relationship between turning radius and other performance variables.
Table 2. Two-way ANOVA for best GLM showing relationship between turning radius and other performance variables.
Sum SqMean SqNum DfDen DFFp
Angular velocity0.3700.370192.3757.5690.007 **
Turn speed0.7740.774196.12615.833<0.001 ***
Pre-turn speed0.2830.283178.4445.7880.018 *
Angular velocity + turn speed0.4500.450196.0409.2160.003 **
Turn speed + pre-turn speed0.2500.250190.9675.1200.026 *
Linear mixed effect models fit by REML; ID was used as a random fixed effect. Observations = 106, N = 36. DenDF (denominator degrees of freedom, NumDF (numerator degrees of freedom). SumSq (sum of squares), MeanSq (mean of squares). F-values are indicated. * p < 0.05; ** p < 0.01; *** p < 0.001. Significant results are in bold type.
Table 3. Two-way ANOVA for best GLM showing relationship between angular velocity and other performance variables.
Table 3. Two-way ANOVA for best GLM showing relationship between angular velocity and other performance variables.
Sum SqMean SqNum DfDen DFFp
Radius0.9010.901199.178169.676<0.001
Turn speed0.3120.312184.83458.781<0.001
Linear mixed effect models fit by REML; ID was used as a random fixed effect. Observations = 106, N = 36. DenDF (denominator degrees of freedom, NumDF (numerator degrees of freedom). SumSq (sum of squares), MeanSq (mean of squares). F-values are indicated.
Table 4. Results from SMA Regression showing body scaling between male and female bandicoots. Slope and elevation comparisons shown within and between groups. Likelihood ratio statistic and Wald’s statistic have df = 1.
Table 4. Results from SMA Regression showing body scaling between male and female bandicoots. Slope and elevation comparisons shown within and between groups. Likelihood ratio statistic and Wald’s statistic have df = 1.
Body
Measurement		SexSlope (95% CI)Intercept (95% CI)R2pSlope
Comparison (Likelihood Ratio)		pElevation
Comparison (Wald’s
Statistic)		p
Body LengthMale0.34 (0.30, 0.38)3.16 (2.88, 3.44)0.97<0.00010.79510.37350.07940.7781
Female0.40 (0.28, 5.61)2.77 (1.84, 3.70)0.86<0.0001
Hind Limb lengthMale0.32 (0.29, 0.34)2.36 (2.20, 2.52)0.99<0.00014.6730.03061.64500.1996
Female0.43 (0.33, 0.58)1.55 (0.73, 2.38)0.88<0.0001
Fore Limb LengthMale0.30 (0.27, 0.34)1.97 (1.7, 2.19)0.98<0.00018.6770.00320.00540.9413
Female0.51 (0.37, 0.70)0.60 (−0.48, 1.67)0.790.0002
Foot LengthMale0.20 (0.17, 0.25)2.45 (2.14, 2.76)0.97<0.00015.5050.01891.5740.2096
Female0.34 (0.24, 0.37)1.58 (0.82, 2.35)0.91<0.0001
Hind digit LengthMale0.30 (0.22, 0.40)0.30 (−0.34, 0.95)0.87<0.00014.0830.04333.9320.0473
Female−0.56 (−0.97, −0.33)6.03 (3.93, 8.14)0.570.0374
Palm Length Male0.24 (0.18, 0.33)1.18 (0.66, 1.70)0.92<0.00018.9060.00280.00860.9261
Female058 (0.36, 0.94)−1.08 (−2.99, 0.83) 0.540.0289
Fore digit LengthMale0.37 (0.27, 0.51)0.28 (−0.55, 1.11)0.79<0.00011.4270.23225.0180.0025
Female0.53 (0.31, 0.86)−0.67 (−2.44, 1.10)0.610.0119
Head WidthMale0.26 (0.23, 0.30)1.72 (1.49, 1.94)098<0.00010.40860.52280.7790.3774
Female0.24 (0.17, 0.33)1.91 (1.38, 2.44)0.96<0.0001
Head LengthMale0.22 (0.19, 0.24)2.95 (2.77, 3.14)0.98<0.00014.5520.03280.66900.4133
Female0.31 (0.23, 0.43)2.31 (1.66, 2.98)0.93<0.0001
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Del Simone, K.; Cameron, S.F.; Clemente, C.J.; Dick, T.J.M.; Wilson, R.S. Biomechanical Trade-Offs Between Speed and Agility in the Northern Brown Bandicoot. Biomechanics 2025, 5, 52. https://doi.org/10.3390/biomechanics5030052

AMA Style

Del Simone K, Cameron SF, Clemente CJ, Dick TJM, Wilson RS. Biomechanical Trade-Offs Between Speed and Agility in the Northern Brown Bandicoot. Biomechanics. 2025; 5(3):52. https://doi.org/10.3390/biomechanics5030052

Chicago/Turabian Style

Del Simone, Kaylah, Skye F. Cameron, Christofer J. Clemente, Taylor J. M. Dick, and Robbie S. Wilson. 2025. "Biomechanical Trade-Offs Between Speed and Agility in the Northern Brown Bandicoot" Biomechanics 5, no. 3: 52. https://doi.org/10.3390/biomechanics5030052

APA Style

Del Simone, K., Cameron, S. F., Clemente, C. J., Dick, T. J. M., & Wilson, R. S. (2025). Biomechanical Trade-Offs Between Speed and Agility in the Northern Brown Bandicoot. Biomechanics, 5(3), 52. https://doi.org/10.3390/biomechanics5030052

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