Population Dynamics: Mathematical Modeling and Reality
- Authors: Medvinsky A.B.1, Adamovich B.V.2, Rusakov A.V.1, Tikhonov D.A.1,3, Nurieva N.I.1, Tereshko V.M.1,4
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Affiliations:
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences
- Belarusian State University
- Institute of Mathematical Problems of Biology, Keldysh Institute of Applied Mathematics
- United Institute of Informatics Problems, National Academy of Sciences of Belarus
- Issue: Vol 64, No 6 (2019)
- Pages: 956-977
- Section: Complex Systems Biophysics
- URL: https://ogarev-online.ru/0006-3509/article/view/153155
- DOI: https://doi.org/10.1134/S0006350919060150
- ID: 153155
Cite item
Abstract
Application of mathematical modeling for analysis of natural phenomena relies on model reduction techniques, which inevitably raises the question of whether the results of simulation reflect real processes. This work analyzes problems that arise when the results obtained by mathematical modeling of population processes are compared to data collected by monitoring of natural ecosystems. The source of these problems is that the type of dependencies between variables that describe the population dynamics, as well as the choice of numerical values assigned to the parameters of the mathematical model, are often impossible to justify, even based on the monitoring data from a particular ecosystem. This paper proposes an approach to mathematical modeling that would take the impact of the entire complex of biotic and abiotic factors on the population dynamics into account. Its central feature is consideration of ecosystem monitoring data and incorporating them directly into mathematical models of population dynamics. This approach would make it possible, in particular, to evaluate the extent to which individual environmental factors influence both the variations in population abundance recorded during monitoring and those characteristics of population processes that are not directly measured during monitoring, but are obtained by mathematical modeling.
About the authors
A. B. Medvinsky
Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences
Author for correspondence.
Email: alexander_medvinsky@yahoo.com
Russian Federation, Pushchino, Moscow oblast, 142290
B. V. Adamovich
Belarusian State University
Email: alexander_medvinsky@yahoo.com
Belarus, Minsk, 220030
A. V. Rusakov
Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences
Email: alexander_medvinsky@yahoo.com
Russian Federation, Pushchino, Moscow oblast, 142290
D. A. Tikhonov
Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences; Institute of Mathematical Problems of Biology, Keldysh Institute of Applied Mathematics
Email: alexander_medvinsky@yahoo.com
Russian Federation, Pushchino, Moscow oblast, 142290; Pushchino, Moscow oblast, 142290
N. I. Nurieva
Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences
Email: alexander_medvinsky@yahoo.com
Russian Federation, Pushchino, Moscow oblast, 142290
V. M. Tereshko
Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences; United Institute of Informatics Problems, National Academy of Sciences of Belarus
Email: alexander_medvinsky@yahoo.com
Russian Federation, Pushchino, Moscow oblast, 142290; Minsk, 220012
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