Evolutionary race as predators hunt prey

Remote-sensing data for wild animals such as lions reveal that predators and prey optimize manoeuvrability rather than speed during the hunt.

Andrew A. Biewener

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The survival of predators and prey depends on their respective abilities to successfully chase food and escape capture, thereby exerting strong selective pressure on their running ability and behavioural strategies. Perhaps nowhere on Earth does this play out more dramatically than on the African savannah, where the fastest terrestrial predators chase their fleet-footed prey.


Yet direct measures of the key factors driving this type of hunt performance in the wild are difficult to obtain. In a paper published in Nature, Wilson et al.¹ report findings from their use of data-capturing collars to track the movement dynamics of wild animals in Botswana during hunts. The authors also conducted computer modelling of predator–prey interactions and carried out laboratory tests to assess the properties of the animals’ muscles.

Read the paper: Biomechanics of predator–prey arms race in lion, zebra, cheetah and impala

In recent years, the ability to use remote-sensing devices under natural field conditions and over long time frames has led many to study animals’ migratory^2,3, foraging⁴ and collective-movement behaviour^5,6, which has provided fascinating insights into biomechanics, physiology and decision-making. Wilson and colleagues took a remote-sensing approach to study lions preying on zebras, and cheetahs preying on impala, in the wild. The authors temporarily immobilized animals and fitted them with lightweight collars containing technically sophisticated, custom-designed, miniature electronic and Global Positioning System (GPS) devices. The devices monitored the animals’ location, movement direction and acceleration patterns. Wilson et al. tracked 9 lions, 5 cheetahs, 7 zebras and 7 impala, and recorded 2,726 high-speed runs for lions, 520 for cheetahs, 1,801 for zebras and 515 for impala. This remarkable data set logs individual animal strides and provides information about the speed, acceleration and turning performance of these predator–prey pairs.

The animals were not observed directly, and one limitation of the recorded data is that few, if any, of the movement tracks represented hunts between pairs of predator and prey, with both animals recorded as one hunts the other. Therefore, the hunting strategies of predator and prey must be inferred from the collar-recorded data, making the assumption that the movement patterns represent actual hunts. However, the locomotor performance recorded by the remote-sensing collars and the hunting strategies that could be inferred from these measurements are consistent with behavioural observations made by others⁷. Moreover, analysis of the full data set revealed that predators and prey exhibited manoeuvrability near the limits of their capability. Hence, although recordings of one-on-one hunts are lacking, the data were consistent with maximal predator-pursuit and prey-evasion performance, enabling the authors to model hunt outcomes.

After collar placement, a tiny biopsy of hindlimb muscle was taken from the animals for subsequent state-of-the-art laboratory testing of single-muscle-fibre contractility. This revealed that, compared with the muscle fibres sampled from the prey species, the predator muscle fibres deliver more power for a given muscle mass when they contract, allowing the predators to run faster and accelerate and decelerate more quickly than their prey. With more-powerful muscles than their prey and claws to grip the ground effectively, predators are better at accelerating into a turn (centripetal acceleration) than their prey are.

Wilson and colleagues’ acceleration and GPS recordings indicated that, during inferred hunts, the predators and prey regularly achieved their maximal turning performance but ran at speeds well below their athletic capabilities.

Running at speeds slower than maximum capacity during a pursuit enhances manoeuvrability, which improves the prey’s probability of successful escape and enables predators to better track their prey’s movements, thereby increasing the number of successful hunts.
Using their field-recorded locomotion data, Wilson and colleagues modelled predator and prey capture–evasion tactics to examine how different performance metrics, such as speed, separation distance between the animals, deceleration, acceleration and turning rate, would affect the outcome of a hunt. Evasion modelling showed that prey escape was more likely if a prey animal relied on turning more sharply and at a greater rate than its pursuer.

This type of behaviour increases the unpredictability of the prey’s movement trajectory, as has also been observed for bipedal desert rodents fleeing a predator⁸. Wilson et al. noted that, during the predators’ approach (Fig. 1), they exhibited greater deceleration and acceleration than that of the prey, allowing the predators to close in on and better track the prey’s lateral movements. The close match of athletic performance between predators and prey highlights the strong selection pressure that has resulted in an evolutionary ‘arms race’ for improved locomotion ability in large carnivores and their large herbivorous prey.

Figure 1 | A lioness hunting a zebra in Etosha National Park, Namibia. Wilson et al.¹ report their analysis of the movement dynamics of predator–prey hunts in the wild in Africa using data gathered remotely from Global Positioning System sensing collars placed on lions, zebras, cheetahs and impala. Credit: Getty

The increasing use of remote-sensing technologies in animal studies is enabling the monitoring of factors such as animal acceleration, pressure (for example, during flight or when swimming at depth) and temperature. Such work promises to illuminate not only predator–prey interactions, but also how wild animals cope with other real-world issues^9,10. For example, this type of research could enhance our understanding of how animals are dealing with the impacts of climate change, or offer insight into the factors governing behaviours such as habitat selection, mating and foraging. Moreover, understanding how animals move might inspire the design of robots that can negotiate complex environments.

Nature 554, 176-178 (2018)

doi: 10.1038/d41586-018-01278-w