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Individuals of different interacting populations often adjust to prevailing conditions by changing their behavior simultaneously, with consequences for trophic relationships throughout the system. While we now have a good theoretical understanding of how individuals adjust their behavior, the population dynamical consequences of co-adaptive behaviors are rarely described. Further, mechanistic descriptions of ecosystem functions are based on population models that seldom take behavior into account. Here, we present a model that combines the population dynamics and adaptive behavior of organisms of two populations simultaneously. We explore how the Nash equilibrium of a system - i.e. the optimal behavior of its constituent organisms - can shape population dynamics, and conversely how population dynamics impact the Nash equilibrium of the system. We illustrate this for the case of diel vertical migration (DVM), the daily movement of marine organisms between food-depleted but safe dark depths and more risky nutrition-rich surface waters. DVM represents the archetypal example of populations choosing between a foraging arena (the upper sunlit ocean) and a refuge (the dark depths). We show that population sizes at equilibrium are significantly different if organisms can adapt their behavior, and that optimal DVM behaviors within the community vary significantly if population dynamics are considered. As a consequence, ecosystem function estimates such as trophic transfer efficiency and vertical carbon export differ greatly when fitness seeking behavior is included. Ignoring the role of behavior in multi-trophic population modeling can potentially lead to inaccurate predictions of population biomasses and ecosystem functions.
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http://dx.doi.org/10.1016/j.jtbi.2021.110663 | DOI Listing |
Philos Trans A Math Phys Eng Sci
September 2025
School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, Tyne and Wear NE1 7RU, UK.
Chemotaxis allows swimming bacteria to navigate through chemical landscapes. To date, continuum models of chemotactic populations (e.g.
View Article and Find Full Text PDFPhilos Trans A Math Phys Eng Sci
September 2025
Department of Bioscience and Engineering, Shibaura Institute of Technology, Saitama, Japan.
The physical environment exerts a profound influence on microbial life. The directional movement of cells in response to their physical environment is understood as taxis, which has been studied in biology as chemotaxis, phototaxis, gravitaxis and so forth. These taxis are induced by physiological, physical or both factors.
View Article and Find Full Text PDFNeotrop Entomol
September 2025
Dept of Entomology, Federal Univ of Viçosa, Viçosa, MG, Brazil.
The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. Thus, the objective of this work was to determine a prediction model for the seasonal dynamics of A.
View Article and Find Full Text PDFJ Math Biol
September 2025
Department of Mathematics, Texas A&M University, Mailstop 3368, College Station, TX, 77843-3368, United States.
We study how environmental stochasticity influences the long-term population size in certain one- and two-species models. The difficulty is that even when one can prove that there is coexistence, it is usually impossible to say anything about the invariant probability measure which describes the coexisting species. We are able to circumvent this problem for some important ecological models by noticing that the per-capita growth rates at stationarity are zero, something which can sometimes yield information about the invariant probability measure.
View Article and Find Full Text PDFJ Math Biol
September 2025
School of Mathematical Sciences and Institute of Natural Sciences, MOE-LSC, CMA-Shanghai, Shanghai Jiao Tong University, Shanghai, China.
It has been noticed that when the waiting time distribution exhibits a transition from an intermediate time power-law decay to a long-time exponential decay in the continuous time random walk model, a transition from anomalous diffusion to normal diffusion can be observed at the population level. However, the mechanism behind the transition of waiting time distribution is rarely studied. In this paper, we provide one possible mechanism to explain the origin of such a transition.
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