Population Dynamics: Mathematical Modeling and Reality

被引:5
作者
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 ]
机构
[1] Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, 142290, Moscow oblast
[2] Belarusian State University, Minsk
[3] Institute of Mathematical Problems of Biology, Keldysh Institute of Applied Mathematics, Pushchino, 142290, Moscow oblast
[4] United Institute of Informatics Problems, National Academy of Sciences of Belarus, Minsk
基金
俄罗斯基础研究基金会;
关键词
mathematical modeling; population dynamics; time series analysis;
D O I
10.1134/S0006350919060150
中图分类号
学科分类号
摘要
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. © 2019, Pleiades Publishing, Inc.
引用
收藏
页码:956 / 977
页数:21
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