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      Multiple preferred escape trajectories are explained by a geometric model incorporating prey’s turn and predator attack endpoint

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          Abstract

          The escape trajectory (ET) of prey – measured as the angle relative to the predator’s approach path – plays a major role in avoiding predation. Previous geometric models predict a single ET; however, many species show highly variable ETs with multiple preferred directions. Although such a high ET variability may confer unpredictability to avoid predation, the reasons why animals prefer specific multiple ETs remain unclear. Here, we constructed a novel geometric model that incorporates the time required for prey to turn and the predator’s position at the end of its attack. The optimal ET was determined by maximizing the time difference of arrival at the edge of the safety zone between the prey and predator. By fitting the model to the experimental data of fish Pagrus major, we show that the model can clearly explain the observed multiple preferred ETs. By changing the parameters of the same model within a realistic range, we were able to produce various patterns of ETs empirically observed in other species (e.g., insects and frogs): a single preferred ET and multiple preferred ETs at small (20–50°) and large (150–180°) angles from the predator. Our results open new avenues of investigation for understanding how animals choose their ETs from behavioral and neurosensory perspectives.

          eLife digest

          When a prey spots a predator about to pounce, it turns swiftly and accelerates away to avoid being captured. The initial direction the prey chooses to take – known as its escape trajectory – can greatly impact their chance of survival.

          Previous models were able to predict the optimal direction an animal should take to maximize its chances of evading the predator. However, experimental data suggest that prey actually tend to escape via multiple specific directions, although why animals use this approach has not been clarified. To investigate this puzzle, Kawabata et al. built a new mathematical model that better represents how prey and predators interact with one another in the real world.

          Unlike past models, Kawabata et al. incorporated the time required for prey to change direction and only allowed the predators to move toward the prey for a limited distance. By including these two factors, they were able to reproduce the escape trajectories of real animals, including a species of fish, as well as species from other taxa such as frogs and insects.

          The new model suggests that prey escape along one of two directions: either by moving directly away from the predator in order to outrun its attack, or by dodging sideways to avoid being captured. Which strategy the prey chooses has some elements of unpredictability, which makes it more difficult for predators to adjust their capturing method.

          These findings shed light on why escaping in multiple specific directions makes prey harder to catch. The model could also be extended to test the escape trajectories of a wider variety of predator and prey species, which may avoid capture via different routes. This could help researchers better understand how predators and prey interact with one another. The findings could also reveal how sensory information (such as sound and sight) associated with the threat of an approaching predator is processed and stimulates the muscle activity required to escape in multiple different directions.

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          Mixed effects models and extensions in ecology with R

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            Biostatistical Analysis

            Zar's Biostatistical Analysis, Fifth Edition , is the ideal book for readers seeking practical coverage of statistical analysis methods used by researchers to collect, summarize, analyze and draw conclusions from biological research. The latest edition of this best-selling textbook is both comprehensive and easy to read. It is suitable as an introduction for beginners and as a comprehensive reference book for biological researchers and other advanced users. Introduction; Populations and Samples; Measures of Central Tendency; Measures of Dispersion and Variability; Probabilities; The Normal Distribution; One-Sample Hypotheses; Two-Sample Hypotheses; Paired-Sample Hypotheses; Multisample Hypotheses: The Analysis of Variance; Multiple Comparisons; Two-Factor Analysis of Variance; Data Transformations; Multiway Factorial Analysis of Variance; Nested (Hierarchical) Analysis of Variance; Multivariate Analysis of Variance; Simple Linear Regression; Comparing Simple Linear Regression Equations; Simple Linear Correlation; Multiple Regression and Correlation; Polynomial Regression; Testing for Goodness of Fit; Contingency Tables; More on Dichotomous Variables; Testing for Randomness; Circular Distributions: Descriptive Statistics; Circular Distributions: Hypothesis Testing For all readers interested in biostatistics.
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              Optimal strategies for predator avoidance: the relative importance of speed and manoeuvrability.

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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                15 February 2023
                2023
                : 12
                : e77699
                Affiliations
                [1 ] Graduate School of Fisheries and Environmental Sciences, Nagasaki University ( https://ror.org/058h74p94) Nagasaki Japan
                [2 ] Faculty of Fisheries, Nagasaki University ( https://ror.org/058h74p94) Nagasaki Japan
                [3 ] The Institute of Statistical Mathematics ( https://ror.org/03jcejr58) Tachikawa Japan
                [4 ] Institute for East China Sea Research, Organization for Marine Science Technology, Nagasaki University ( https://ror.org/058h74p94) Nagasaki Japan
                [5 ] National Institute for Basic Biology ( https://ror.org/05q8wtt20) Okazaki Japan
                [6 ] CNR-IAS, Località Sa Mardini ( https://ror.org/013fk0013) Oristano Italy
                [7 ] CNR-IBF, Area di Ricerca San Cataldo ( https://ror.org/041xzk838) Pisa Italy
                University of St Andrews ( https://ror.org/02wn5qz54) United Kingdom
                University of St Andrews ( https://ror.org/02wn5qz54) United Kingdom
                University of St Andrews ( https://ror.org/02wn5qz54) United Kingdom
                University of St Andrews ( https://ror.org/02wn5qz54) United Kingdom
                Harvard University Center for Brain Science ( https://ror.org/03vek6s52) United States
                Author information
                https://orcid.org/0000-0001-8267-5199
                https://orcid.org/0000-0003-3710-2564
                Article
                77699
                10.7554/eLife.77699
                10065801
                36790147
                314df0ad-7281-4d3c-8055-9f3e790c1df1
                © 2023, Kawabata et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 08 February 2022
                : 13 February 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: Grants-in-Aid for Young Scientists B: 17K17949
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008608, Sumitomo Foundation;
                Award ID: Grant for Environmental Research Projects: 153128
                Award Recipient :
                Funded by: ISM Cooperative Research Program;
                Award ID: 2014-ISM.CRP-2006
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: Grant-in-Aid for Scientific Research on Innovative Areas: 19H04936
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Ecology
                Evolutionary Biology
                Custom metadata
                The mathematical model incorporating new parameters explains multimodal distributions in escape direction (i.e., multiple preferred escape trajectories), which are previously observed in various animal taxa.

                Life sciences
                escape direction,escape response,escape turn,matching law,mathematical model,predator evasion,other

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