The pursuit strategy of predatory bluefish (<i>Pomatomus saltatrix</i>)

McHenry, Matthew J.; Johansen, Jacob L.; Soto, Álvaro; Free, Brian A; Paley, Derek A.; Liao, James C.

doi:10.1098/rspb.2018.2934

Cited by 24 publications

(31 citation statements)

References 17 publications

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“…Section 4 applies PAM to a case study of the pursuit tactics of bluefish predators and §5 uses the methodology on a case study on the evasion tactics of zebrafish prey. Although the primary data analysed in these two cases is based on two previous experimental studies [28,29], the results reported here can be verified using the statistical data provided in this work. Section 6 summarizes the results and describes ongoing work.…”

Section: Introductionsupporting

confidence: 70%

Probabilistic analytical modelling of predator–prey interactions in fishes

Free

McHenry

Paley

2019

J. R. Soc. Interface.

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Predation is a fundamental interaction between species, yet it is largely unclear what tactics are successful for the survival or capture of prey. One challenge in this area comes with how to test theoretical ideas about strategy with experimental measurements of features such as speed, flush distance and escape angles. Tactics may be articulated with an analytical model that predicts the motion of predator or prey as they interact. However, it may be difficult to recognize how the predictions of such models relate to behavioural measurements that are inherently variable. Here, we present an alternative approach for modelling predator -prey interactions that uses deterministic dynamics, yet incorporates experimental kinematic measurements of natural variation to predict the outcome of biological events. This technique, called probabilistic analytical modelling (PAM), is illustrated by the interactions between predator and prey fish in two case studies that draw on recent experiments. In the first case, we use PAM to model the tactics of predatory bluefish (Pomatomus saltatrix) as they prey upon smaller fish (Fundulus heteroclitus). We find that bluefish perform deviated pure pursuit with a variable pursuit angle that is suboptimal for the time to capture. In the second case, we model the escape tactics of zebrafish larvae (Danio rerio) when approached by adult predators of the same species. Our model successfully predicts the measured patterns of survivorship using measured probability density functions as parameters. As these results demonstrate, PAM is a data-driven modelling approach that can be predictive, offers analytical transparency, and does not require numerical simulations of system dynamics. Though predator -prey interactions demonstrate the use of this technique, PAM is not limited to studying biological systems and has broad utility that may be applied towards understanding a wide variety of natural and engineered dynamical systems where data-driven modelling is beneficial.

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Section: Introductionsupporting

confidence: 70%

Probabilistic analytical modelling of predator–prey interactions in fishes

Free

McHenry

Paley

2019

J. R. Soc. Interface.

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“…Therefore, we could straightforwardly derive the optimal 117 prediction rule adopted by predators by using a straight line ( figure 1c). This is similar to 118 interception strategies which have been observed in birds, bats, fishes, and insects [31][32][33][34][35]. The 119 predicted position can be described as follows: 120…”

Section: Lévy Walks With µ = 11 and 30 Correspond Approximately To supporting

confidence: 63%

Optimal Random Avoidance Strategy in Prey-Predator Interactions

Abe

Kasada

2020

Preprint

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16It has recently been reported that individual animals, ranging from insects to birds and mammals, 17 exhibit a special class of random walks, known as Lévy walks, which can lead to higher search 18 efficiency than normal random walks. However, the role of randomness or unpredictability in 19 animal movements is not very well understood. In the present study, we used a theoretical 20 framework to explore the advantage of Lévy walks in terms of avoidance behaviour in prey-21 predator interactions and analysed the conditions for maximising the prey's survival rate. We 22 showed that there is a trade-off relationship between the predictability of the prey's movement 23 and the length of time of exposure to predation risk, suggesting that it is difficult for prey to 24 decrease both parameters in order to survive. Then, we demonstrated that the optimal degree of 25 randomness in avoidance behaviour could change depending on the predator's ability. In 26 particular, Lévy walks resulted in higher survival rates than normal random walks and straight 27 movements when the physical ability of the predators was high. This indicates that the advantage 28 of Lévy walks may also be present in random avoidance behaviour and provides new insights into 29 why Lévy walks can evolve in terms of randomness. 30 31 Keywords: Lévy walks, avoidance behaviour, prey-predator interactions, movement ecology 32 33 34 35 36

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“…In hoverflies Eristalis and Volucella , males use their innate knowledge of female’s size to compute the optimum interception angle based on the combination of position and angular speed of the target 26 . Other species maintain the bias angle constant throughout the pursuits such as Bluefish Pomatomus saltatrix , who keeps a 10° horizontal bias angle 27 . Dragonflies use a biased pursuit strategy in the vertical plane to hold the target image in the dorsal acute zone, a crescent of a particularly high resolution about 55° above the eye equator.…”

Section: Introductionmentioning

confidence: 99%

Two pursuit strategies for a single sensorimotor control task in blowfly

Varennes

Krapp

Viollet

2020

Sci Rep

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Effective visuomotor coordination is a necessary requirement for the survival of many terrestrial, aquatic, and aerial animal species. We studied the kinematics of aerial pursuit in the blowfly Lucilia sericata using an actuated dummy as target for freely flying males. We found that the flies perform target tracking in the horizontal plane and target interception in the vertical plane. Our behavioural data suggest that the flies’ trajectory changes are a controlled combination of target heading angle and of the rate of change of the bearing angle. We implemented control laws in kinematic models and found that the contributions of proportional navigation strategy are negligible. We concluded that the difference between horizontal and vertical control relates to the difference in target heading angle the fly keeps constant: 0° in azimuth and 23° in elevation. Our work suggests that male Lucilia control both horizontal and vertical steerings by employing proportional controllers to the error angles. In horizontal plane, this controller operates at time delays as small as 10 ms, the fastest steering response observed in any flying animal, so far.

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The pursuit strategy of predatory bluefish (Pomatomus saltatrix)

Cited by 24 publications

References 17 publications

Probabilistic analytical modelling of predator–prey interactions in fishes

Probabilistic analytical modelling of predator–prey interactions in fishes

Optimal Random Avoidance Strategy in Prey-Predator Interactions

Two pursuit strategies for a single sensorimotor control task in blowfly

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