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Revealing Evolutionarily Optimal Strategies in Self-Reproducing Systems via a New Computational Approach

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journal contribution
posted on 19.05.2020, 15:10 by Simran Kaur Sandhu, Andrew Morozov, Oleg Kuzenkov
Modelling the evolution of complex life history traits and behavioural patterns observed in the natural world is a challenging task. Here, we develop a novel computational method to obtain evolutionarily optimal life history traits/behavioural patterns in population models with a strong inheritance. The new method is based on the reconstruction of evolutionary fitness using underlying equations for population dynamics and it can be applied to self-reproducing systems (including complicated age-structured models), where fitness does not depend on initial conditions, however, it can be extended to some frequency-dependent cases. The technique provides us with a tool to efficiently explore both scalar-valued and function-valued traits with any required accuracy. Moreover, the method can be implemented even in the case where we ignore the underlying model equations and only have population dynamics time series. As a meaningful ecological case study, we explore optimal strategies of diel vertical migration (DVM) of herbivorous zooplankton in the vertical water column which is a widespread phenomenon in both oceans and lakes, generally considered to be the largest synchronised movement of biomass on Earth. We reveal optimal trajectories of daily vertical motion of zooplankton grazers in the water column depending on the presence of food and predators. Unlike previous studies, we explore both scenarios of DVM with static and dynamic predators. We find that the optimal pattern of DVM drastically changes in the presence of dynamic predation. Namely, with an increase in the amount of food available for zooplankton grazers, the amplitude of DVM progressively increases, whereas for static predators DVM would abruptly cease.

History

Citation

Bulletin of Mathematical Biology volume 81, pages4701–4725(2019)

Author affiliation

Department of Mathematics

Version

VoR (Version of Record)

Published in

Bulletin of Mathematical Biology

Volume

81

Issue

11

Pagination

4701 - 4725

Publisher

SPRINGER

issn

0092-8240

eissn

1522-9602

Acceptance date

05/09/2019

Copyright date

2019

Available date

18/11/2019

Publisher version

https://link.springer.com/article/10.1007/s11538-019-00663-4

Spatial coverage

England

Language

English